Direct Optimization: A direct minimization approach that avoids SCF iterations, enabling smooth convergence and natural integration of machine learning methods, and advanced quantum chemistry methods into density functional theory calculations.
@@ -83,7 +83,7 @@ Call Graph
-
+
This project is a `JAX
`_-based package for differantiable density functional theory computation of solids.
@@ -92,7 +92,7 @@ This project is a `JAX
`_-based package for diff
Installation
------------
-You may install directly from GitHub, using the following command. This can be used to obtain the most recent version of Optax:
+You may install directly from GitHub, using the following command. This can be used to obtain the most recent version of ``jrystal``:
.. code-block:: console
diff --git a/docs/installation.rst b/docs/installation.rst
index d919bfa..44678f8 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -16,14 +16,6 @@ Installation Guide
We strongly recommend user to install the cuda version of ``JAX`` for better performance, if you have a GPU. ``jrystal`` can be run on CPU, but it is not optimized for CPU and not recommended.
-Install from PyPI
-~~~~~~~~~~~~~~~~
-
-The easiest way to install ``jrystal`` is via pip:
-
-.. code-block:: console
-
- $ pip install jrystal
Install from Source
~~~~~~~~~~~~~~~~~
@@ -36,6 +28,22 @@ For the latest development version, you can install from source:
$ cd jrystal
$ pip install -e .
+
+Install from PyPI
+~~~~~~~~~~~~~~~~
+
+.. warning::
+
+ Install via PyPI is not supported yet. PyPI installation will be available in the future.
+
+
+The easiest way to install ``jrystal`` is via pip:
+
+.. code-block:: console
+
+ $ pip install jrystal
+
+
Verify Installation
~~~~~~~~~~~~~~~~~
@@ -45,6 +53,7 @@ To verify that jrystal is installed correctly, you can run:
$ python -c "import jrystal; print(jrystal.__version__)"
+
Troubleshooting
~~~~~~~~~~~~~
diff --git a/docs/roadmap.rst b/docs/roadmap.rst
index 9330a7e..e714b39 100644
--- a/docs/roadmap.rst
+++ b/docs/roadmap.rst
@@ -56,7 +56,7 @@ Career Opportunities
We're growing! Join our team at `Sea AI Lab
`_:
-- `Full-time positions
`_
+- `Full-time positions `_
- `Part-time positions `_
We welcome contributions from both academia and industry. Whether you're looking to join our team or contribute as a community member, we're excited to work with you!
diff --git a/jrystal/__init__.py b/jrystal/__init__.py
index a2aeb7f..3eea64c 100644
--- a/jrystal/__init__.py
+++ b/jrystal/__init__.py
@@ -14,26 +14,27 @@
__version__ = "0.0.1"
-from . import _src
-from . import crystal
-from .crystal import Crystal
-from . import calc
-
-from . import pseudopotential
-from . import utils
-from . import sbt
-from . import pw
-from . import energy
-from . import entropy
-from . import occupation
-from . import potential
-from . import hamiltonian
-from . import grid
-from . import ewald
-
-from . import config
from pathlib import Path
+from . import (
+ _src,
+ calc,
+ config,
+ crystal,
+ energy,
+ entropy,
+ ewald,
+ grid,
+ hamiltonian,
+ occupation,
+ potential,
+ pseudopotential,
+ pw,
+ sbt,
+ utils
+)
+from .crystal import Crystal
+
def get_pkg_path():
return str(Path(__file__).parent.parent)
diff --git a/jrystal/_src/__init__.py b/jrystal/_src/__init__.py
index 29fa56f..f02d1a1 100644
--- a/jrystal/_src/__init__.py
+++ b/jrystal/_src/__init__.py
@@ -12,21 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-from . import band
-from . import pw
-from . import energy
-from . import entropy
-from . import occupation
-from . import const
-from . import crystal
-from . import potential
-from . import hamiltonian
-from . import kinetic
-from . import ewald
-from . import grid
-from . import utils
-from . import unitary_module
-from . import braket
+from . import (
+ band,
+ braket,
+ const,
+ crystal,
+ energy,
+ entropy,
+ ewald,
+ grid,
+ hamiltonian,
+ kinetic,
+ occupation,
+ potential,
+ pw,
+ spmd,
+ unitary_module,
+ utils,
+ xc
+)
__all__ = [
"band",
@@ -44,4 +48,6 @@
"utils",
"unitary_module",
"braket",
+ "spmd",
+ "xc",
]
diff --git a/jrystal/_src/band/__init__.py b/jrystal/_src/band/__init__.py
index a3de46f..975bae8 100644
--- a/jrystal/_src/band/__init__.py
+++ b/jrystal/_src/band/__init__.py
@@ -11,10 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-
"""Functions for band structure calculation."""
-
from .k_path import get_k_path
__all__ = ['get_k_path']
diff --git a/jrystal/_src/band/k_path.py b/jrystal/_src/band/k_path.py
index cb8e665..0968c39 100644
--- a/jrystal/_src/band/k_path.py
+++ b/jrystal/_src/band/k_path.py
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-
"""Module for operations related to band structure optimization.
This module is a wrapper of ASE.BandPath modules.
@@ -19,9 +18,9 @@
"""
-from ase import cell
import numpy as np
-from jaxtyping import Float, Array
+from ase import cell
+from jaxtyping import Array, Float
from jrystal._src.const import BOHR2ANGSTROM
diff --git a/jrystal/_src/braket.py b/jrystal/_src/braket.py
index 860a12f..5a3057d 100644
--- a/jrystal/_src/braket.py
+++ b/jrystal/_src/braket.py
@@ -16,10 +16,11 @@
This module provides functions for calculating inner products (brakets) and expectation values in both real and reciprocal space, which are fundamental operations in quantum mechanics and density functional theory (DFT) calculations.
"""
+from typing import Optional, Union
+
import einops
import jax.numpy as jnp
import numpy as np
-from typing import Union, Optional
from jaxtyping import Array, Complex, Float
@@ -33,7 +34,7 @@ def reciprocal_braket(
Computes the inner product between two wavefunctions in reciprocal space using:
.. math::
-
+
\langle f|g \rangle \approx \sum_{\mathbf{G}} f^*(\mathbf{G})g(\mathbf{G}) \frac{vol}{N^2}
where :math:`f^*(\mathbf{G})` is the complex conjugate of :math:`f(\mathbf{G})`, :math:`N` is the number of grid points, and :math:`vol` is the real-space unit cell volume.
@@ -54,7 +55,7 @@ def reciprocal_braket(
Raises:
ValueError: If bra and ket shapes do not match.
"""
- if bra.shape != ket.shape:
+ if bra.shape[-3:] != ket.shape[-3:]:
raise ValueError(
f"bra and ket shape are not aligned. Got "
f"{bra.shape} and {ket.shape}."
@@ -133,7 +134,7 @@ def expectation(
E_{ij} = \langle \psi_i | \hat{H} | \psi_j \rangle \approx \sum_{\mathbf{q}} \psi_i^*(\mathbf{q}) \hat{H}(\mathbf{q}) \psi_j(\mathbf{q}) \frac{vol}{N^p}
where :math:`\psi_i` and :math:`\psi_j` are wavefunctions, :math:`\hat{H}` is the Hamiltonian operator, :math:`\mathbf{q}` represents either real (:math:`\mathbf{r}`) or reciprocal (:math:`\mathbf{G}`) space coordinates, and :math:`p` depends on the mode:
-
+
- For real space: :math:`p = 1`
- For reciprocal space: :math:`p = 2` (includes Parseval factor)
- For kinetic terms: :math:`p = 0`
diff --git a/jrystal/_src/crystal.py b/jrystal/_src/crystal.py
index 1f91810..45fc71b 100644
--- a/jrystal/_src/crystal.py
+++ b/jrystal/_src/crystal.py
@@ -18,14 +18,15 @@
hartree for energy and Bohr for length. The input positions of atoms should be
in angstrom.
"""
+from typing import List, Optional, Union
+
import ase
-from ase.io import read
-import numpy as np
import jax.numpy as jnp
-from jaxtyping import Float, Array
-from typing import Optional, Union, List
-
+import numpy as np
+from ase.io import read
from chex import dataclass
+from jaxtyping import Array, Float
+
from .const import ANGSTROM2BOHR
diff --git a/jrystal/_src/energy.py b/jrystal/_src/energy.py
index 90ca0ca..4fe05e3 100644
--- a/jrystal/_src/energy.py
+++ b/jrystal/_src/energy.py
@@ -12,14 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Energy functions. """
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
import jax.numpy as jnp
import numpy as np
-from typing import Tuple
from jaxtyping import Array, Complex, Float, Int
-from . import braket, potential, pw
+from . import braket, potential, pw, xc
from .ewald import ewald_coulomb_repulsion
from .grid import translation_vectors
from .utils import (
@@ -28,7 +27,7 @@
def hartree(
- density_grid_reciprocal: Complex[Array, 'x y z'],
+ density_grid_reciprocal: Complex[Array, 'spin x y z'],
g_vector_grid: Float[Array, 'x y z 3'],
vol: Float,
kohn_sham: bool = False
@@ -47,7 +46,7 @@ def hartree(
The Hartree energy is computed as:
.. math::
-
+
E_H = \frac{1}{2}\sum_{\mathbf{G}} \hat{\rho}(\mathbf{G})\hat{V}_{H}(\mathbf{G})
= 2\pi \sum_{\mathbf{G}} \frac{|\hat{\rho}(\mathbf{G})|^2}{|\mathbf{G}|^2}
@@ -55,24 +54,27 @@ def hartree(
for more details.
Args:
- density_grid_reciprocal (Complex[Array, 'x y z']): Electron density in
- reciprocal space.
- g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal space.
+ density_grid_reciprocal (Complex[Array, 'spin x y z']): Electron density in
+ reciprocal space. The input density must contains spin axis.
+ g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal
+ space.
vol (Float): Unit cell volume.
- kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
+ kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to
+ False.
Returns:
Float: Hartree energy.
"""
dim = g_vector_grid.shape[-1]
- if density_grid_reciprocal.ndim == dim + 1:
- density_grid_reciprocal = jnp.sum(density_grid_reciprocal, axis=0)
+ assert density_grid_reciprocal.ndim == dim + 1, (
+ 'density_grid_reciprocal must contains spin axis'
+ )
v_hartree_reciprocal = potential.hartree_reciprocal(
density_grid_reciprocal, g_vector_grid, kohn_sham
)
-
+ v_hartree_reciprocal = jnp.expand_dims(v_hartree_reciprocal, axis=0)
hartree_energy = braket.reciprocal_braket(
v_hartree_reciprocal, density_grid_reciprocal, vol
)
@@ -81,7 +83,7 @@ def hartree(
def external(
- density_grid_reciprocal: Complex[Array, 'x y z'],
+ density_grid_reciprocal: Complex[Array, 'spin x y z'],
position: Float[Array, 'atom 3'],
charge: Float[Array, 'atom'],
g_vector_grid: Float[Array, 'x y z 3'],
@@ -93,8 +95,8 @@ def external(
expressing the real-space integral as a sum over reciprocal lattice vectors:
.. math::
- E = \sum_{\mathbf{G}} \hat{\rho}(\mathbf{G})\hat{V}_{\text{ext}}(\mathbf{G}) =
- \sum_{\mathbf{G}} \hat{\rho}(\mathbf{G}) \sum_{\alpha} Z_{\alpha}
+ E = \sum_{\mathbf{G}} \hat{\rho}(\mathbf{G})\hat{V}_{\text{ext}}(\mathbf{G}) =
+ \sum_{\mathbf{G}} \hat{\rho}(\mathbf{G}) \sum_{\alpha} Z_{\alpha}
\exp(-i\mathbf{G}\cdot\mathbf{R}_{\alpha}) v(\mathbf{G})
where:
@@ -106,8 +108,8 @@ def external(
- :math:`v(\mathbf{G})` is the Fourier transform of the Coulomb potential
Args:
- density_grid_reciprocal (Complex[Array, 'x y z']): Electron density in
- reciprocal space.
+ density_grid_reciprocal (Complex[Array, 'spin x y z']): Electron density in
+ reciprocal space. The input density must contains spin axis.
position (Float[Array, 'atom 3']): Atomic positions in the unit cell.
charge (Float[Array, 'atom']): Nuclear charges.
g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal space.
@@ -117,12 +119,15 @@ def external(
Float: External potential energy.
"""
dim = g_vector_grid.shape[-1]
- if density_grid_reciprocal.ndim == dim + 1:
- density_grid_reciprocal = jnp.sum(density_grid_reciprocal, axis=0)
+
+ assert density_grid_reciprocal.ndim == dim + 1, (
+ 'density_grid_reciprocal must contains spin axis'
+ )
v_external_reciprocal = potential.external_reciprocal(
position, charge, g_vector_grid, vol
)
+ v_external_reciprocal = jnp.expand_dims(v_external_reciprocal, axis=0)
external_energy = braket.reciprocal_braket(
v_external_reciprocal, density_grid_reciprocal, vol
)
@@ -142,11 +147,11 @@ def kinetic(
reciprocal space. The kinetic energy is computed as:
.. math::
- E_{\text{kin}} = \frac{1}{2} \sum_{\mathbf{G}}
+ E_{\text{kin}} = \frac{1}{2} \sum_{\mathbf{G}}
|\mathbf{k} + \mathbf{G}|^2 |c_{n\mathbf{k}}(\mathbf{G})|^2
where:
-
+
- :math:`\mathbf{k}` is the k-point vector
- :math:`\mathbf{G}` is the reciprocal lattice vector
- :math:`c_{n\mathbf{k}}(\mathbf{G})` are the plane wave coefficients
@@ -177,43 +182,33 @@ def kinetic(
return safe_real(e_kin)
-def xc_lda(
- density_grid: Float[Array, 'x y z'],
+def xc_energy(
+ density_grid: Float[Array, 'spin x y z'],
+ g_vector_grid: Float[Array, 'x y z 3'],
vol: Float,
+ xc_type: str,
kohn_sham: bool = False
) -> Float:
- r"""Calculate the LDA exchange-correlation energy.
-
- Implements the Local Density Approximation (LDA) for the exchange-correlation
- energy in the spin-unpolarized case. The exchange energy in LDA is given by:
-
- .. math::
- E_x^{\text{LDA}}[\rho] = -\frac{3}{4}\left(\frac{3}{\pi}\right)^{1/3}
- \int \rho(\mathbf{r})^{4/3} d\mathbf{r}
-
- Note that this implementation includes both exchange and correlation terms,
- though only the exchange term is shown in the equation above.
+ r"""Calculate the exchange-correlation energy of the input density.
Args:
- density_grid (Float[Array, 'x y z']): Real-space electron density.
+ density_grid (Float[Array, 'spin x y z']): Real-space electron density.
+ The input density must contains spin axis.
vol (Float): Unit cell volume.
kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
Returns:
- Float: LDA exchange-correlation energy.
+ Float: exchange-correlation energy.
"""
- assert density_grid.ndim in [3, 4]
+ assert density_grid.ndim == 4, ('density_grid must contains spin axis')
- if density_grid.ndim == 4: # have spin channel
- density_grid = jnp.sum(density_grid, axis=0)
+ num_grid = jnp.prod(jnp.array(density_grid.shape[-3:]))
+ exc_density = xc.xc_density(density_grid, g_vector_grid, kohn_sham, xc_type)
+ e_xc = jnp.sum(exc_density * density_grid)
+ e_xc = safe_real(e_xc)
- num_grid = jnp.prod(jnp.array(density_grid.shape))
- lda_density = potential.xc_lda(density_grid, kohn_sham)
- e_lda = jnp.sum(lda_density * density_grid)
- e_lda = safe_real(e_lda)
-
- return e_lda * vol / num_grid
+ return e_xc * vol / num_grid
def nuclear_repulsion(
@@ -257,7 +252,7 @@ def total_energy(
vol: Float,
occupation: Optional[Float[Array, "spin kpt band"]] = None,
kohn_sham: bool = False,
- xc: str = 'lda',
+ xc: str = 'lda_x',
split: bool = False,
) -> Union[Float, Tuple[Float, Float, Float, Float]]:
r"""Calculate the total electronic energy of the system.
@@ -266,11 +261,11 @@ def total_energy(
Hartree, and exchange-correlation terms:
.. math::
-
- E_{\\text{tot}} = E_{\\text{kin}} + E_{\\text{ext}} + E_H + E_{xc}
+
+ E_{\text{tot}} = E_{\text{kin}} + E_{\text{ext}} + E_H + E_{xc}
.. warning::
-
+
This function does not include the nuclear-nuclear repulsion (Ewald) energy. For the complete total energy, the Ewald term must be added separately.
Args:
@@ -304,10 +299,7 @@ def total_energy(
e_kin = kinetic(g_vector_grid, kpts, coefficient, occupation)
e_ext = external(density_grid_rec, position, charge, g_vector_grid, vol)
e_har = hartree(density_grid_rec, g_vector_grid, vol, kohn_sham)
- if xc == 'lda':
- e_xc = xc_lda(density_grid, vol, kohn_sham)
- else:
- raise NotImplementedError(f"xc {xc} is not supported yet.")
+ e_xc = xc_energy(density_grid, g_vector_grid, vol, xc, kohn_sham)
if split:
return e_kin, e_ext, e_har, e_xc
@@ -324,7 +316,7 @@ def band_energy(
vol: Float,
occupation: Float[Array, "spin kpt band"],
kohn_sham: bool = False,
- xc: str = 'lda'
+ xc_type: str = "lda_x"
):
r"""Calculate the energy eigenvalues for each electronic state.
@@ -332,7 +324,7 @@ def band_energy(
single-particle Hamiltonian for each state:
.. math::
-
+
\varepsilon_{n\mathbf{k}} = \langle \psi_{n\mathbf{k}} |
\hat{T} + \hat{V}_{\text{eff}} | \psi_{n\mathbf{k}} \rangle
@@ -348,7 +340,7 @@ def band_energy(
vol (Float): Unit cell volume.
occupation (Float[Array, "spin kpt band"]): Occupation numbers.
kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
- xc (str, optional): Exchange-correlation functional type. Defaults to 'lda'.
+ xc_type (str, optional): Exchange-correlation functional type. Defaults to 'lda'.
Returns:
Float[Array, "spin kpt band"]: Energy eigenvalues for each electronic state.
@@ -363,7 +355,7 @@ def band_energy(
g_vector_grid,
vol,
split=False,
- xc=xc,
+ xc_type=xc_type,
kohn_sham=kohn_sham
)
e_eff = braket.real_braket(density_per_band, v_eff, vol)
diff --git a/jrystal/_src/grid.py b/jrystal/_src/grid.py
index 0710132..2fc7323 100644
--- a/jrystal/_src/grid.py
+++ b/jrystal/_src/grid.py
@@ -22,15 +22,15 @@
- Grid transformations between real and reciprocal space
'''
import itertools
-from typing import List, Tuple, Union
+from typing import List, Optional, Tuple, Union
import jax
import jax.numpy as jnp
import numpy as np
-from typing import Optional
from ase.dft.kpoints import monkhorst_pack
from jax import lax
from jaxtyping import Array, Bool, Float, Int
+
from .utils import fft_factor
@@ -443,8 +443,8 @@ def g2cell_vectors(
'''
grid_sizes = g_vector_grid.shape[:-1]
cardinality = g_vectors(jnp.eye(3), grid_sizes)
- a = g_vector_grid.reshape([-1, 3])
- b = cardinality.reshape([-1, 3])
+ a = cardinality.reshape([-1, 3])
+ b = g_vector_grid.reshape([-1, 3])
return jnp.linalg.inv(jnp.linalg.inv(a.T @ a) @ a.T @ b)
diff --git a/jrystal/_src/hamiltonian.py b/jrystal/_src/hamiltonian.py
index 9287845..31af603 100644
--- a/jrystal/_src/hamiltonian.py
+++ b/jrystal/_src/hamiltonian.py
@@ -16,27 +16,29 @@
This module provides functions to compute Hamiltonian matrix elements and related quantities in a plane wave basis. The Hamiltonian includes both kinetic and effective potential terms, supporting both standard and Kohn-Sham DFT calculations.
"""
+from typing import Union
+
import jax
import jax.numpy as jnp
-from jaxtyping import Float, Array, Int, Complex
-from typing import Union
+from jaxtyping import Array, Complex, Float, Int
-from .kinetic import kinetic_operator
-from . import pw, braket, potential, utils
+from . import braket, potential, pw, utils
from .hessian import complex_hessian
+from .kinetic import kinetic_operator
def _hamiltonian_matrix(
coefficient: Complex[Array, "spin kpt band x y z"],
positions: Float[Array, "atom 3"],
charges: Int[Array, "atom"],
- effictive_density_grid: Float[Array, "x y z"],
+ effictive_density_grid: Union[Float[Array, "x y z"],
+ Float[Array, "spin x y z"]],
g_vector_grid: Float[Array, "x y z 3"],
kpts: Float[Array, "kpt 3"],
vol: Float,
- xc: str = 'lda',
+ xc: str = 'lda_x',
kohn_sham: bool = False
-) -> Float[Array, "kpt band band"]:
+) -> Float[Array, "spin kpt band band"]:
r"""Compute the Hamiltonian matrix elements between orbitals.
The Hamiltonian matrix (hamiltonian-orbital matrix) is defined as:
@@ -46,7 +48,7 @@ def _hamiltonian_matrix(
H_{ij} = \langle \psi_i | \hat{H} | \psi_j \rangle
where :math:`\hat{H}` is the Hamiltonian operator composed of kinetic and effective potential terms:
-
+
.. math::
\hat{H} = \hat{T} + \hat{V}_{eff}
@@ -54,12 +56,21 @@ def _hamiltonian_matrix(
The kinetic term :math:`\hat{T}` is computed in reciprocal space, while the effective potential term :math:`\hat{V}_{eff}` includes both the external potential from ions and the electron-electron interaction terms.
Args:
- coefficient (Complex[Array, "spin kpt band x y z"]): Plane wave coefficients with shape [spin, kpt, band, x, y, z]. The last three dimensions represent the spatial grid.
- positions (Float[Array, "atom 3"]): Atomic positions in Bohr units with shape [atom, 3].
- charges (Int[Array, "atom"]): Atomic numbers for each atom with shape [atom].
- effictive_density_grid (Float[Array, "x y z"]): Electron density for effective potential evaluated on real space grid with shape [x, y, z].
- g_vector_grid (Float[Array, "x y z 3"]): G-vector grid in reciprocal space with shape [x, y, z, 3].
- kpts (Float[Array, "kpt 3"]): K-points in reciprocal space with shape [kpt, 3].
+ coefficient (Complex[Array, "spin kpt band x y z"]): Plane wave
+ coefficients with shape [spin, kpt, band, x, y, z]. The last three
+ dimensions represent the spatial grid.
+ positions (Float[Array, "atom 3"]): Atomic positions in Bohr units with
+ shape [atom, 3].
+ charges (Int[Array, "atom"]): Atomic numbers for each atom with shape
+ [atom].
+ effictive_density_grid (Union[Float[Array, "x y z"],
+ Float[Array, "spin x y z"]]): Electron density for effective potential
+ evaluated on real space grid with shape [x, y, z]. It can contains spin
+ axis or not.
+ g_vector_grid (Float[Array, "x y z 3"]): G-vector grid in reciprocal space
+ with shape [x, y, z, 3].
+ kpts (Float[Array, "kpt 3"]): K-points in reciprocal space with shape
+ [kpt, 3].
vol (Float): Volume of the unit cell.
xc (str): Exchange-correlation functional name. Defaults to 'lda'.
kohn_sham: Whether to use Kohn-Sham potential. Defaults to False.
@@ -67,6 +78,8 @@ def _hamiltonian_matrix(
Returns:
Float[Array, "kpt band band"]: Float array of shape [kpt, band, band] containing the Hamiltonian matrix elements between all pairs of bands at each k-point.
"""
+ if effictive_density_grid.ndim == 4:
+ effictive_density_grid = jnp.sum(effictive_density_grid, axis=0)
v_eff = potential.effective(
effictive_density_grid,
@@ -77,8 +90,8 @@ def _hamiltonian_matrix(
split=False,
xc=xc,
kohn_sham=kohn_sham
- )
- wave_grid = pw.wave_grid(coefficient, vol)
+ ) # [x y z]
+ wave_grid = pw.wave_grid(coefficient, vol) # [spin kpt band x y z]
f_eff = braket.expectation(wave_grid, v_eff, vol, diagonal=False, mode="real")
t_kin = kinetic_operator(g_vector_grid, kpts)
@@ -86,22 +99,22 @@ def _hamiltonian_matrix(
coefficient, t_kin, vol, diagonal=False, mode='kinetic'
)
- return (f_eff + f_kin)[0]
+ return (f_eff + f_kin)
def hamiltonian_matrix_trace(
band_coefficient: Complex[Array, "spin kpt band x y z"],
positions: Float[Array, "atom 3"],
charges: Int[Array, "atom"],
- effictive_density_grid: Float[Array, "x y z"],
+ effictive_density_grid: Union[Float[Array, "x y z"],
+ Float[Array, "spin x y z"]],
g_vector_grid: Float[Array, "x y z 3"],
kpts: Float[Array, "kpt 3"],
vol: Float,
- xc: str = 'lda',
+ xc: str = 'lda_x',
kohn_sham: bool = True,
keep_kpts_axis: bool = False,
- keep_spin_axis: bool = False,
-) -> Union[Float, Float[Array, "spin"], Float[Array, "spin kpt"]]:
+) -> Union[Float[Array, "spin"], Float[Array, "spin kpt"]]:
r"""Calculate the trace of the Hamiltonian matrix.
The trace is computed as:
@@ -124,21 +137,12 @@ def hamiltonian_matrix_trace(
xc (str): Exchange-correlation functional name. Defaults to 'lda'.
kohn_sham (bool): Whether to use Kohn-Sham potential. Defaults to True.
keep_kpts_axis (bool): If True, retains the k-points axis in output. Defaults to False.
- keep_spin_axis (bool): If True, retains the spin axis in output. Defaults to False.
Returns:
- Union[Float, Float[Array, "spin"], Float[Array, "spin kpt"]]: The trace of the Hamiltonian matrix. The shape depends on keep_kpts_axis and keep_spin_axis:
-
- - If both False: returns a scalar Float
- - If keep_spin_axis True: returns Float[Array, "spin"]
- - If keep_kpts_axis True: returns Float[Array, "kpt"]
- - If both True: returns Float[Array, "spin kpt"]
+ Union[Float[Array, "spin"], Float[Array, "spin kpt"]]: The trace of the
+ Hamiltonian matrix. The output has shape [spin, kpt] if keep_kpts_axis is
+ :code:`True`, otherwise [spin].
"""
- num_spin = band_coefficient.shape[0]
- if num_spin == 2:
- raise NotImplementedError(
- "Spin-unrestricted hamiltonian matrix is not implemented."
- )
v_eff = potential.effective(
effictive_density_grid,
@@ -148,7 +152,7 @@ def hamiltonian_matrix_trace(
vol,
False,
xc,
- kohn_sham
+ kohn_sham,
)
wave_grid = pw.wave_grid(band_coefficient, vol)
f_eff = braket.expectation(wave_grid, v_eff, vol, diagonal=True, mode="real")
@@ -157,28 +161,27 @@ def hamiltonian_matrix_trace(
f_kin = braket.expectation(
band_coefficient, t_kin, vol, diagonal=True, mode='kinetic'
) # [spin, kpt, band]
-
hamil_trace = (f_eff + f_kin).real
- if not keep_kpts_axis:
- hamil_trace = jnp.sum(hamil_trace, axis=1)
- if not keep_spin_axis:
- hamil_trace = jnp.sum(hamil_trace, axis=0)
- return jnp.sum(hamil_trace, axis=-1)
+ if keep_kpts_axis:
+ return jnp.sum(hamil_trace, axis=(1, 2))
+ else:
+ return jnp.sum(hamil_trace, axis=(0, 1, 2))
def hamiltonian_matrix(
band_coefficient: Complex[Array, "spin kpt band x y z"],
positions: Float[Array, "atom 3"],
charges: Int[Array, "atom"],
- effictive_density_grid: Float[Array, "x y z"],
+ effictive_density_grid: Union[Float[Array, "x y z"],
+ Float[Array, "spin x y z"]],
g_vector_grid: Float[Array, "x y z 3"],
kpts: Float[Array, "kpt 3"],
vol: Float,
- xc: str = 'lda',
+ xc: str = 'lda_x',
kohn_sham: bool = True,
-) -> Complex[Array, "kpt band band"]:
+) -> Complex[Array, "spin kpt band band"]:
r"""Compute the full Hamiltonian matrix in the orbital basis.
-
+
This function computes the complete Hamiltonian matrix including both diagonal
and off-diagonal elements. The matrix elements are defined as:
@@ -192,31 +195,25 @@ def hamiltonian_matrix(
band_coefficient (Complex[Array, "spin kpt band x y z"]): Plane wave coefficients with shape [spin, kpt, band, x, y, z]. The last three dimensions represent the spatial grid.
positions (Float[Array, "atom 3"]): Atomic positions in Bohr units with shape [atom, 3].
charges (Int[Array, "atom"]): Atomic numbers for each atom with shape [atom].
- effictive_density_grid (Float[Array, "x y z"]): Electron density for effective potential evaluated on real space grid with shape [x, y, z].
+ effictive_density_grid (Union[Float[Array, "x y z"], Float[Array, "spin x y z"]]): Electron density for effective potential evaluated on real space grid with shape [x, y, z]. It can contains spin axis or not.
g_vector_grid (Float[Array, "x y z 3"]): G-vector grid in reciprocal space with shape [x, y, z, 3].
- kpts (Float[Array, "kpt 3"]): K-points in reciprocal space with shape [kpt, 3].
+ kpts (Float[Array, "kpt 3"]): K-points in reciprocal space with shape [kpt, 3]. Currently, only spin-restricted
+ calculation enable parallel calculation for multiple K-points.
vol (Float): Volume of the unit cell.
xc (str): Exchange-correlation functional name. Defaults to 'lda'.
kohn_sham (bool): Whether to use Kohn-Sham potential. Defaults to True.
Returns:
- Complex[Array, "kpt band band"]: Complex array of shape [kpt, band, band] containing the complete Hamiltonian matrix elements between all pairs of bands at each k-point.
+ Complex[Array, "spin kpt band band"]: Complex array of shape [spin, kpt, band, band] containing the complete Hamiltonian matrix elements between all pairs of bands at each k-point.
"""
num_bands = band_coefficient.shape[-4]
- num_spin = band_coefficient.shape[0]
-
- if num_spin == 2:
- raise NotImplementedError(
- "Spin-unrestricted hamiltonian matrix is not implemented."
- )
def hamil_k(k, coeff_k):
k = jnp.reshape(k, [-1, 3])
- coeff_k = jnp.expand_dims(coeff_k, axis=1)
def efun(u):
- _coeff = jnp.einsum("i, *n i a b c -> *n a b c", u, coeff_k)
- _coeff = jnp.expand_dims(_coeff, axis=-4)
+ _coeff = jnp.einsum("i,ixyz->xyz", u, coeff_k)
+ _coeff = jnp.expand_dims(_coeff, axis=range(3))
band_energies = hamiltonian_matrix_trace(
_coeff,
@@ -229,14 +226,18 @@ def efun(u):
xc,
kohn_sham,
)
-
return 0.5 * jnp.sum(band_energies).astype(band_coefficient.dtype)
x = jnp.ones(num_bands, dtype=band_coefficient.dtype)
return complex_hessian(efun, x)
- h1 = jax.vmap(hamil_k, in_axes=(0, 1), out_axes=0)(kpts, band_coefficient)
- return h1 # shape: [kpt, band, band]
+ h = jax.vmap(
+ lambda coeff: jax.vmap(hamil_k, in_axes=(0, 0), out_axes=0)(kpts, coeff)
+ )(
+ band_coefficient
+ )
+
+ return h
def _hamiltonian_matrix_basis(
@@ -247,11 +248,11 @@ def _hamiltonian_matrix_basis(
g_vector_grid: Float[Array, "x y z 3"],
kpts: Float[Array, "kpt 3"],
vol: Float,
- xc: str = 'lda',
+ xc: str = 'lda_x',
kohn_sham: bool = True,
) -> Complex[Array, "kpt band band"]:
r"""Compute the Hamiltonian matrix in the plane wave basis.
-
+
This internal function computes the Hamiltonian matrix elements in the plane wave basis set defined by the frequency mask. It uses complex hessian calculations to efficiently compute the matrix elements.
Args:
diff --git a/jrystal/_src/hessian.py b/jrystal/_src/hessian.py
index 24a186f..8510bb7 100644
--- a/jrystal/_src/hessian.py
+++ b/jrystal/_src/hessian.py
@@ -12,16 +12,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Hessian for Complex-Valued Functions. """
+from typing import Callable
+
import jax
import jax.numpy as jnp
-from typing import Callable
def complex_hessian(
f: Callable[[jnp.ndarray], jnp.ndarray],
primal: jnp.ndarray,
) -> jnp.ndarray:
- """Compute the Hessian of a complex-valued function at a point.
+ """Compute the Hessian of a complex-valued function at a point.
.. warning::
diff --git a/jrystal/_src/occupation.py b/jrystal/_src/occupation.py
index 7071200..4057035 100644
--- a/jrystal/_src/occupation.py
+++ b/jrystal/_src/occupation.py
@@ -12,33 +12,45 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Occupation module."""
+from typing import Optional, Union
+
import einops
+import jax
import jax.numpy as jnp
-from typing import Optional
-from jaxtyping import Float, Array
+from jax.lax import stop_gradient
+from jaxtyping import Array, Float
+
from .unitary_module import unitary_matrix, unitary_matrix_param_init
+from .utils import check_spin_number
-def idempotent_param_init(key: Array, num_bands: int, num_kpts: int) -> dict:
+def idempotent_param_init(
+ key: Array,
+ num_bands: int,
+ num_electrons: int,
+ num_kpts: int,
+ spin: int = 0,
+ spin_restricted: bool = True,
+) -> dict:
r"""Initialize the parameters for the idempotent occupation.
-
+
This function is an implementation of the method proposed in:
-
+
`Li, Tianbo, et al. "Diagonalization without Diagonalization: A Direct Optimization Approach for Solid-State Density Functional Theory." arXiv preprint arXiv:2411.05033 (2024).`
-
+
Please also refer to the tutorial :doc:`Occupation <../tutorial/occupation>` for more details.
-
+
Examples:
.. code:: python
-
+
from jrystal import occupation
key = jax.random.PRNGKey(0)
num_kpts = 1
num_electrons = 10
- # number of bands must be greater than or equal to
+ # number of bands must be greater than or equal to
# (num_electrons + 1) // 2 for spin-restricted calculation.
- num_bands = 10
+ num_bands = 10
params = occupation.idempotent_param_init(key, num_bands, num_kpts)
occ = occupation.idempotent(params, num_electrons, num_kpts, spin=0)
@@ -46,78 +58,90 @@ def idempotent_param_init(key: Array, num_bands: int, num_kpts: int) -> dict:
Args:
key (Array): The random key for initialization.
num_bands (int): The number of bands.
+ num_electrons (int): The number of electrons.
num_kpts (int): The number of :math:`k`-points.
+ spin (int, optional): The number of unpaired electrons. Defaults to 0.
Returns:
dict: A dictionary containing the initialized parameters.
"""
- return unitary_matrix_param_init(
- key, [num_bands * num_kpts, num_bands * num_kpts], complex=False
+ check_spin_number(num_electrons, spin)
+
+ num_elec_up = (num_electrons + spin) // 2
+ num_elec_down = (num_electrons - spin) // 2
+
+ param_up = unitary_matrix_param_init(
+ key, [num_bands * num_kpts, num_elec_up * num_kpts], complex=False
)
+ if spin_restricted:
+ return {"param_up": param_up, "param_down": param_up}
+
+ else:
+ param_down = unitary_matrix_param_init(
+ key, [num_bands * num_kpts, num_elec_down * num_kpts], complex=False
+ )
+ return {"param_up": param_up, "param_down": param_down}
def idempotent(
params: dict,
- num_electrons: int,
num_kpts: int,
- spin: int = 0,
spin_restricted: bool = True,
) -> Float[Array, 'spin kpt band']:
r"""Calculate the occupation number using the idempotent method.
-
+
This function is an implementation of the method proposed in:
-
+
`Li, Tianbo, et al. "Diagonalization without Diagonalization: A Direct Optimization Approach for Solid-State Density Functional Theory." arXiv preprint arXiv:2411.05033 (2024).`
-
+
Please also refer to the tutorial :doc:`Occupation <../tutorial/occupation>` for more details.
-
+
Examples:
.. code:: python
-
+
from jrystal import occupation
key = jax.random.PRNGKey(0)
num_kpts = 1
num_electrons = 10
- # number of bands must be greater than or equal to
+ # number of bands must be greater than or equal to
# (num_electrons + 1) // 2 for spin-restricted calculation.
- num_bands = 10
+ num_bands = 10
params = occupation.idempotent_param_init(key, num_bands, num_kpts)
occ = occupation.idempotent(params, num_electrons, num_kpts, spin=0)
Args:
params (dict): The parameters for the idempotent occupation. It can be generated by :func:`idempotent_param_init`.
- num_electrons (int): The number of electrons.
num_kpts (int): The number of :math:`k`-points.
- spin (int, optional): The number of unpaired electrons. Defaults to 0.
spin_restricted (bool, optional): Whether to use spin-restricted calculation. Defaults to True.
Returns:
Float[Array, 'spin kpt band']: The occupation number.
-
+
"""
- num_bands = params["w_re"].shape[0] // num_kpts
+ param_up = params["param_up"]
+ param_down = params["param_down"]
+
+ _up_shape = param_up["w_re"].shape
+ _down_shape = param_down["w_re"].shape
+
+ num_bands = _up_shape[0] // num_kpts
- def o(num_e):
+ def o(params):
u = unitary_matrix(params, False)
- indices = jnp.arange(num_bands * num_kpts)
- mask_ones = indices < num_e * num_kpts
- mask_fraction = indices == jnp.floor(num_e * num_kpts)
- fraction = num_e * num_kpts - jnp.floor(num_e * num_kpts)
- f = jnp.where(mask_ones, 1.0, jnp.where(mask_fraction, fraction, 0.0))
- occ = einops.einsum(u, f, u.T, "nk ik, ik, ik nk -> nk")
- return occ.reshape([num_kpts, num_bands]) / num_kpts
-
- occ = jnp.stack(
- [o((num_electrons - spin) // 2), o((num_electrons + spin) // 2)], axis=0
- )
+ # [num_bands * num_kpts, num_elec_up * num_kpts]
+ occ = einops.einsum(u, u.T, "nk ik, ik nk -> nk")
+ return occ.reshape([num_kpts, num_bands])
- if spin_restricted is False:
- return occ
+ occ_up = o(param_up)
+ occ_down = o(param_down)
+
+ if spin_restricted:
+ return jnp.expand_dims(occ_up + occ_down, axis=0) / num_kpts
else:
- return jnp.sum(occ, axis=0, keepdims=True)
+ return jnp.stack([occ_up, occ_down], axis=0) / num_kpts
def uniform(
@@ -211,3 +235,132 @@ def gamma(
return jnp.sum(occ, axis=0, keepdims=True)
return occ
+
+
+def param_init(
+ key: Array,
+ num_bands: int,
+ num_electrons: int,
+ num_kpts: int,
+ spin: int = 0,
+ method: str = "simplex-projector",
+ spin_restricted: bool = True
+) -> Union[dict, Float[Array, 'spin kpt band']]:
+ if method == "idempotent":
+ return idempotent_param_init(key, num_bands, num_electrons, num_kpts, spin)
+ elif method == "uniform":
+ return uniform(num_kpts, num_electrons, spin, num_bands, spin_restricted)
+ elif method == "gamma":
+ return gamma(num_kpts, num_electrons, spin, num_bands, spin_restricted)
+ elif method == "simplex-projector":
+ return simplex_projector_init(num_bands, num_kpts)
+ else:
+ raise ValueError(f"Invalid method: {method}")
+
+
+def occupation(
+ params: dict,
+ num_kpts: int,
+ num_electrons: Optional[int] = None,
+ spin: int = 0,
+ method: str = "simplex-projector",
+ spin_restricted: bool = True
+) -> Float[Array, 'spin kpt band']:
+ if method == "idempotent":
+ return idempotent(params, num_kpts, spin_restricted)
+ elif method == "uniform":
+ return stop_gradient(params)
+ elif method == "gamma":
+ return stop_gradient(params)
+ elif method == "simplex-projector":
+ return simplex_projector(params, num_electrons, spin, spin_restricted)
+ else:
+ raise ValueError(f"Invalid method: {method}")
+
+
+def simplex_projector_init(
+ num_bands: int,
+ num_kpts: int,
+) -> dict:
+ n = num_bands * num_kpts
+ params_up = (jnp.arange(n) - n//2)*0.1
+ params_up = params_up.reshape([num_kpts, num_bands])
+
+ params_down =(jnp.arange(n) - n//2)*0.1
+ params_down = params_down.reshape([num_kpts, num_bands])
+
+ return {"param_up": params_up, "param_down": params_down}
+
+
+def proj(x: jnp.array, sum: jnp.array):
+ """project onto the occupation constraint, such that:
+ sum(x) = sum
+ x >= 0
+ x <= 1
+
+ Args:
+ x (jnp.array): an 1-D array, must be non-negative and less than 1.
+ sum (float): the sum of the output array.
+
+ Returns:
+ jnp.array: an 1-D array, the sum of which is ``sum``.
+ """
+ n = x.shape[0]
+
+ def pushdown(x):
+ x_sorted = jnp.sort(x)
+ x_sum = jnp.sum(x)
+ x_cumsum = jnp.cumsum(x_sorted)
+ _rho = (x_sum - sum - x_cumsum) / (n - jnp.arange(n) - 1)
+ k = jnp.argmax(x_sorted > _rho)
+ _lambda = jax.lax.select(
+ k == 0,
+ (x_sum - sum) / n,
+ (x_sum - sum - x_cumsum.at[k-1].get()) / (n - k)
+ )
+ return jnp.maximum(x - _lambda, 0.)
+
+ def pushup(x):
+ x_sorted = jnp.sort(x)
+ x_sum = jnp.sum(x)
+ x_sorted = x_sorted.at[::-1].get()
+ x_cumsum = jnp.cumsum(x_sorted)
+ _rho = (x_sum - sum + (jnp.arange(n) + 1 - x_cumsum)) / (n-jnp.arange(n)-1)
+ k = jnp.argmax(x_sorted - _rho < 1.)
+ _lambda = jax.lax.select(
+ k == 0,
+ (sum-x_sum) / n,
+ (sum-x_sum-(k- x_cumsum.at[k-1].get())) / (n - k)
+ )
+ return jnp.minimum(x + _lambda, 1.)
+
+ output = jax.lax.select(jnp.sum(x) <= sum, pushup(x), pushdown(x))
+ return output
+
+
+def simplex_projector(
+ params: dict,
+ num_electrons: int,
+ spin: int = 0,
+ spin_restricted: bool = True,
+) -> Float[Array, 'spin kpt band']:
+ num_kpts = params["param_up"].shape[0]
+ num_bands = params["param_up"].shape[1]
+
+ params_up = jax.nn.sigmoid(params["param_up"])
+ params_up = jnp.ravel(params_up)
+ m_up = (num_electrons + spin) // 2 * num_kpts
+ m_down = (num_electrons - spin) // 2 * num_kpts
+ occ_up = proj(params_up, m_up) / num_kpts
+ occ_up = occ_up.reshape([num_kpts, num_bands])
+
+ params_down = jax.nn.sigmoid(params["param_down"])
+ params_down = jnp.ravel(params_down)
+ occ_down = proj(params_down, m_down) / num_kpts
+ occ_down = occ_down.reshape([num_kpts, num_bands])
+ occ = jnp.stack([occ_up, occ_down], axis=0)
+
+ if spin_restricted:
+ return jnp.sum(occ, axis=0, keepdims=True)
+ else:
+ return occ
diff --git a/jrystal/_src/potential.py b/jrystal/_src/potential.py
index 361cdb8..0f3313d 100644
--- a/jrystal/_src/potential.py
+++ b/jrystal/_src/potential.py
@@ -18,9 +18,11 @@
from jax.lax import stop_gradient
from jaxtyping import Array, Complex, Float
+from .xc import xc_density
+
def hartree_reciprocal(
- density_grid_reciprocal: Complex[Array, 'x y z'],
+ density_grid_reciprocal: Complex[Array, 'spin x y z'],
g_vector_grid: Float[Array, 'x y z 3'],
kohn_sham: bool = False
) -> Complex[Array, 'x y z']:
@@ -34,7 +36,7 @@ def hartree_reciprocal(
\quad \hat{V}_H(\mathbf{0}) = 0
where:
-
+
- :math:`\hat{V}_H(\mathbf{G})` is the Hartree potential in reciprocal space
- :math:`\hat{n}(\mathbf{G})` is the electron density in reciprocal space
- :math:`\mathbf{G}` is the reciprocal lattice vector
@@ -43,17 +45,24 @@ def hartree_reciprocal(
for more details.
Args:
- density_grid_reciprocal (Complex[Array, 'x y z']): Electron density in
- reciprocal space.
- g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal space.
- kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
+ density_grid_reciprocal (Complex[Array, 'spin x y z']): Electron density in
+ reciprocal space. The input density must contains spin axis.
+ g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal
+ space.
+ kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to
+ False.
Returns:
Complex[Array, 'x y z']: Hartree potential in reciprocal space. If density_grid_reciprocal has batch axes, they are preserved in the output.
"""
dim = g_vector_grid.shape[-1]
+ assert density_grid_reciprocal.ndim == dim + 1, (
+ 'density_grid_reciprocal must contains spin axis'
+ )
+ density_grid_reciprocal = jnp.sum(density_grid_reciprocal, axis=0)
+
g_vec_square = jnp.sum(g_vector_grid**2, axis=-1) # [x y z]
- g_vec_square = g_vec_square.at[(0,) * dim].set(1e-16)
+ g_vec_square = g_vec_square.at[(0,) * dim].set(1)
if kohn_sham:
density_grid_reciprocal = stop_gradient(density_grid_reciprocal)
@@ -69,7 +78,7 @@ def hartree_reciprocal(
def hartree(
- density_grid_reciprocal: Complex[Array, 'x y z'],
+ density_grid_reciprocal: Complex[Array, 'spin x y z'],
g_vector_grid: Float[Array, 'x y z 3'],
kohn_sham: bool = False
) -> Complex[Array, 'x y z']:
@@ -83,20 +92,26 @@ def hartree(
V_H(\mathbf{r}) = \mathcal{F}^{-1}[\hat{V}_H(\mathbf{G})]
where:
-
+
- :math:`V_H(\mathbf{r})` is the Hartree potential in real space
- :math:`\hat{V}_H(\mathbf{G})` is the Hartree potential in reciprocal space
- :math:`\mathcal{F}^{-1}` denotes the inverse Fourier transform
Args:
- density_grid_reciprocal (Complex[Array, 'x y z']): Electron density in
- reciprocal space.
- g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal space.
- kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
+ density_grid_reciprocal (Complex[Array, 'spin x y z']): Electron density in
+ reciprocal space. The input density must contains spin axis.
+ g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal
+ space.
+ kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to
+ False.
Returns:
Complex[Array, 'x y z']: Hartree potential in real space. If density_grid_reciprocal has batch axes, they are preserved in the output.
"""
+ assert density_grid_reciprocal.ndim == 4, (
+ 'density_grid_reciprocal must contains spin axis'
+ )
+ density_grid_reciprocal = jnp.sum(density_grid_reciprocal, axis=0)
har_pot_grid_rcprl = hartree_reciprocal(
density_grid_reciprocal, g_vector_grid, kohn_sham
)
@@ -167,7 +182,7 @@ def external(
V_{\text{ext}}(\mathbf{r}) = \mathcal{F}^{-1}[\hat{V}_{\text{ext}}(\mathbf{G})]
where:
-
+
- :math:`V_{\text{ext}}(\mathbf{r})` is the external potential in real space
- :math:`\hat{V}_{\text{ext}}(\mathbf{G})` is the external potential in reciprocal space
- :math:`\mathcal{F}^{-1}` denotes the inverse Fourier transform
@@ -185,93 +200,19 @@ def external(
return jnp.fft.ifftn(ext_pot_grid_rcprl, axes=range(-3, 0))
-def _lda_density(
- density_grid: Float[Array, '*d x y z']
-) -> Float[Array, '*d x y z']:
- r"""Calculate the LDA exchange-correlation energy density.
-
- Implements the Local Density Approximation (LDA) for the exchange-correlation
- energy density in the spin-unpolarized case:
-
- .. math::
- \varepsilon_{xc}^{\text{LDA}}[n] = -\frac{3}{4}
- \left(\frac{3}{\pi}\right)^{1/3} n(\mathbf{r})^{1/3}
-
- where:
-
- - :math:`n(\mathbf{r})` is the electron density
- - :math:`\varepsilon_{xc}^{\text{LDA}}` is the exchange-correlation energy density
-
- Args:
- density_grid (Float[Array, '*d x y z']): Real-space electron density.
- May include batch dimensions.
-
- Returns:
- Float[Array, '*d x y z']: LDA exchange-correlation energy density.
- Preserves any batch dimensions from the input.
- """
- t3 = 3**(0.1e1 / 0.3e1)
- t4 = jnp.pi**(0.1e1 / 0.3e1)
- t8 = 2.220446049250313e-16**(0.1e1 / 0.3e1)
- t10 = jnp.where(0.1e1 <= 2.22044604925e-16, t8 * 2.22044604925e-16, 1)
- t11 = density_grid**(0.1e1 / 0.3e1)
- t15 = jnp.where(
- density_grid / 0.2e1 <= 1e-15, 0, -0.3e1 / 0.8e1 * t3 / t4 * t10 * t11
- )
- res = 0.2e1 * 1. * t15
- return res
-
-
-def xc_lda(density_grid: Float[Array, 'x y z'],
- kohn_sham: bool = False) -> Float[Array, 'x y z']:
- r"""Calculate the LDA exchange-correlation potential.
-
- Implements the Local Density Approximation (LDA) for the exchange-correlation
- potential in the spin-unpolarized case. The potential is obtained as the
- functional derivative of the LDA energy:
-
- .. math::
- v_{xc}^{\text{LDA}}(\mathbf{r}) = -\left(\frac{3\rho(\mathbf{r})}{\pi}
- \right)^{1/3}
-
- where:
-
- - :math:`\rho(\mathbf{r})` is the electron density
- - :math:`v_{xc}^{\text{LDA}}` is the exchange-correlation potential
-
- Args:
- density_grid (Float[Array, 'x y z']): Real-space electron density.
- kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
-
- Returns:
- Float[Array, 'x y z']: LDA exchange-correlation potential.
- """
- dim = density_grid.ndim
- if dim > 3:
- density_grid = jnp.sum(density_grid, axis=range(0, dim - 3))
-
- if kohn_sham:
- density_grid = stop_gradient(density_grid)
- output = -(density_grid * 3. / jnp.pi)**(1 / 3)
- else:
- return _lda_density(density_grid)
-
- return output
-
-
def effective(
- density_grid: Float[Array, 'x y z'],
+ density_grid: Float[Array, 'spin x y z'],
position: Float[Array, "num_atom 3"],
charge: Float[Array, "num_atom"],
g_vector_grid: Float[Array, 'x y z 3'],
vol: Float,
split: bool = False,
- xc: str = "lda",
+ xc_type: str = "lda_x",
kohn_sham: bool = False,
- spin_restricted: bool = True,
-) -> Union[Tuple[
- Float[Array, 'x y z'], Float[Array, 'x y z'], Float[Array, 'x y z']],
- Float[Array, 'x y z']]:
+) -> Union[Tuple[Float[Array, '... x y z'],
+ Float[Array, '... x y z'],
+ Float[Array, '... x y z']],
+ Float[Array, '... x y z']]:
r"""Calculate the effective potential for electronic structure calculations.
The effective potential is the sum of three contributions:
@@ -285,33 +226,37 @@ def effective(
- :math:`V_H(\mathbf{r})` is the Hartree potential
- :math:`V_{\text{ext}}(\mathbf{r})` is the external (nuclear) potential
- :math:`V_{xc}(\mathbf{r})` is the exchange-correlation potential
-
+
.. warning::
Currently supports only LDA exchange-correlation functional.
Args:
- density_grid (Float[Array, 'x y z']): Real-space electron density.
+ density_grid (Union[Float[Array, 'x y z'], Float[Array, 'spin x y z']]):
+ Real-space electron density. The dimension of the density grid can be
+ either 3 or 4, where 4 dimension is for spin-polarized calculation (first
+ dimension is spin channel).
position (Float[Array, "num_atom 3"]): Atomic positions in the unit cell.
charge (Float[Array, "num_atom"]): Nuclear charges.
- g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal space.
+ g_vector_grid (Float[Array, 'x y z 3']): Grid of G-vectors in reciprocal
+ space.
vol (Float): Volume of the unit cell.
split (bool, optional): If True, return individual potential components.
Defaults to False.
- xc (str, optional): Exchange-correlation functional type. Only "lda" is
- currently supported. Defaults to "lda".
- kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to False.
+ xc (str, optional): Exchange-correlation functional type. Only "lda_x" is
+ currently supported. Defaults to "lda_x".
+ kohn_sham (bool, optional): If True, use Kohn-Sham formalism. Defaults to
+ False.
spin_restricted (bool, optional): If True, use spin-restricted calculation.
Defaults to True.
Returns:
- Union[Tuple[Array, Array, Array], Array]: If split is True, returns (Hartree, external, exchange-correlation) potentials. Otherwise returns their sum as the total effective potential.
+ Union[Tuple[Array, Array, Array], Array]: If split is True, returns (Hartree, external, xc) potentials. Otherwise returns their sum as the total effective potential. For spin-polarized calculation, the shape of the output is [2, x, y, z], else [x, y, z].
"""
dim = position.shape[-1]
+ assert density_grid.ndim in [dim, dim + 1]
- assert density_grid.ndim in [dim, dim + 1] # w/w\o spin channel
-
- if spin_restricted and density_grid.ndim == dim + 1:
- density_grid = jnp.sum(density_grid, axis=0)
+ if density_grid.ndim == dim:
+ density_grid = jnp.expand_dims(density_grid, 0)
density_grid_reciprocal = jnp.fft.fftn(density_grid, axes=range(-dim, 0))
# reciprocal space:
@@ -321,10 +266,8 @@ def effective(
v_external = external_reciprocal(position, charge, g_vector_grid, vol)
# real space:
- if xc.strip() in ["lda", "lda_x"]:
- v_xc = xc_lda(density_grid, kohn_sham)
- else:
- raise NotImplementedError("XC only support LDA for now.")
+ v_xc: Float[Array, 's x y z']
+ v_xc = xc_density(density_grid, g_vector_grid, kohn_sham, xc_type)
# transform to real space
v_hartree = jnp.fft.ifftn(v_hartree, axes=range(-dim, 0))
@@ -333,4 +276,4 @@ def effective(
if split:
return v_hartree, v_external, v_xc
else:
- return v_hartree + v_external + v_xc
+ return v_hartree[None, ...] + v_external[None, ...] + v_xc
diff --git a/jrystal/_src/pw.py b/jrystal/_src/pw.py
index 487229f..cf8c6d7 100644
--- a/jrystal/_src/pw.py
+++ b/jrystal/_src/pw.py
@@ -17,9 +17,11 @@
import jax
import jax.numpy as jnp
import numpy as np
+from jax.sharding import Sharding
from jaxtyping import Array, Bool, Complex, Float
from .grid import g_vectors
+from .spmd.fft import ifftn3d
from .unitary_module import unitary_matrix, unitary_matrix_param_init
from .utils import absolute_square, expand_coefficient, volume
@@ -30,6 +32,7 @@ def param_init(
num_kpts: int,
freq_mask: Bool[Array, 'x y z'],
spin_restricted: bool = True,
+ sharding: Optional[Sharding] = None
) -> dict:
r'''Initialize the raw parameters.
@@ -53,7 +56,7 @@ def param_init(
.. math::
- \psi(r) = e^{ikr}\sum_G c_{kG} e^{iGr}
+ \psi(r) = e^{i\vb{k}^\top \vb{r}}\sum_G c_{kG} e^{i\vb{G}^\top \vb{r}}
This function generates a raw parameter, which after processing by
:py:func:`coeff` can be used as the :math:`c_{kG}` part of the above equation.
@@ -85,11 +88,13 @@ def param_init(
num_spin = 1 if spin_restricted else 2
num_g = np.sum(freq_mask).item()
shape = (num_spin, num_kpts, num_g, num_bands)
- return unitary_matrix_param_init(key, shape, complex=True)
+ return unitary_matrix_param_init(key, shape, complex=True, sharding=sharding)
def coeff(
- pw_param: Union[dict, Array, Tuple], freq_mask: Bool[Array, 'x y z']
+ pw_param: Union[dict, Array, Tuple],
+ freq_mask: Bool[Array, 'x y z'],
+ sharding: Optional[Sharding] = None
) -> Complex[Array, 'spin kpt band x y z']:
r'''Create the linear coefficients to combine the frequency components.
@@ -128,7 +133,7 @@ def coeff(
constraint that for any :code:`i,j`, :code:`einsum('kabc,labc->kl', ret[i, j], ret[i, j])`
is an identity matrix.
'''
- coeff = unitary_matrix(pw_param, complex=True)
+ coeff = unitary_matrix(pw_param, complex=True, sharding=sharding)
return expand_coefficient(coeff, freq_mask)
@@ -201,7 +206,7 @@ def wave_function(r):
Complex[Array, 'spin kpt band x y z']: Wave function evaluated at the spatial grid.
'''
grid_sizes = coeff.shape[-3:]
- wave_grid = jnp.fft.ifftn(coeff, axes=range(-3, 0))
+ wave_grid = ifftn3d(coeff)
wave_grid *= np.prod(grid_sizes) / jnp.sqrt(vol)
return wave_grid
@@ -209,8 +214,11 @@ def wave_function(r):
def density_grid(
coeff: Complex[Array, 'spin kpt band x y z'],
vol: Float,
- occupation: Optional[Float[Array, 'spin kpt band']] = None
-) -> Union[Float[Array, 'spin kpt band x y z'], Float[Array, 'spin kpt band']]:
+ occupation: Optional[Float[Array, 'spin kpt band']] = None,
+) -> Union[
+ Float[Array, 'spin kpt band x y z'],
+ Float[Array, 'spin x y z'],
+]:
r'''Compute the density at the spatial grid.
In a system with electrons, the density of the electron is the result of
@@ -221,7 +229,7 @@ def density_grid(
.. math::
- \rho(r) = \sum_i |\psi_i(r)|^2
+ \rho(\vb{r}) = \sum_i |\psi_i(\vb{r})|^2
This function evaluates the density :math:`\rho(r)` at the spatial grid
generated from :py:func:`jrystal.grid.r_vectors`.
@@ -231,8 +239,8 @@ def density_grid(
.. math::
- \psi(r) = \frac{1}{\sqrt{\Omega_\text{cell}}} e^{ikr} \sum_G c_{kG}
- e^{iGr}
+ \psi(\vb{r}) = \frac{1}{\sqrt{\Omega_\text{cell}}} e^{i\vb{k}^\top \vb{r}} \sum_G c_{kG}
+ e^{i\vb{G}^\top \vb{r}}
The :math:`c_{kG}` can be computed from :py:func:`param_init`
and :py:func:`coeff`. For calculation of density, we only need
@@ -240,25 +248,34 @@ def density_grid(
.. math::
- \rho(r) = \frac{1}{\Omega_\text{cell}} e^{ikr} \sum_G c_{kG}
- e^{iGr}
+ \rho(\vb{r}) = \frac{1}{\Omega_\text{cell}} \sum_{G, G'} c_{kG}c_{kG'}^*
+ e^{i\vb{(G - G')}^\top \vb{r}}
Args:
- coeff: :math:`c_{kG}` part of the parameter. It can have a leading batch dimension which will be summed to get the overall density. Therefore the shape is :code:`(spin, kpt, band, x, y, z)`.
+ coeff: :math:`c_{kG}` part of the parameter. It can have a leading batch
+ dimension which will be summed to get the overall density. Therefore the
+ shape is :code:`(spin, kpt, band, x, y, z)`.
vol: Volume of the unit cell, a real scalar.
- occupation: The occupation over different k frequencies. The shape is :code:`(spin, kpt, band)`, it should have the same leading dimension as :code:`coeff`. This is an optional argument. When :code:`occupation=None`, we compute the density contribution from each :math:`k` without summing them. If :code:`occupation` is provided, we sum up all the density from each :math:`k` weighted by the occupation.
+ occupation: The occupation over different k frequencies. The shape is
+ :code:`(spin, kpt, band)`, it should have the same leading dimension as
+ :code:`coeff`. This is an optional argument. When
+ :code:`occupation=None`, we compute the density contribution from each
+ :math:`k` without summing them. If :code:`occupation` is provided, we sum
+ up all the density from each :math:`k` weighted by the occupation.
Returns:
- Union[Float[Array, "spin kpt band x y z"], Float[Array, "spin kpt band"]]: A real-valued tensor that represents the density at the spatial grid computed from :py:func:`jrystal.grid.r_vectors`. The shape is :code:`(x, y, z)` if :code:`occupation` is provided, else the shape is :code:`(spin, kpt, band, x, y, z)`.
+ Union[Float[Array, "spin kpt band x y z"], Float[Array, "spin x y z"], Float
+ [Array, "x y z"]]: A real-valued tensor that represents the density at
+ the spatial grid computed from :py:func:`jrystal.grid.r_vectors`. The
+ shape is :code:`(spin, x, y, z)` if :code:`occupation` is provided, else
+ the shape is :code:`(spin, kpt, band, x, y, z)`.
'''
wave_grid_arr = wave_grid(coeff, vol)
dens = absolute_square(wave_grid_arr)
if occupation is not None:
- occ = jnp.expand_dims(occupation, range(-3, 0))
try:
- occ = jnp.broadcast_to(occ, dens.shape)
- dens = jnp.sum(dens * occ, axis=range(occupation.ndim))
+ dens = jnp.einsum('skb...,skb->s...', dens, occupation)
except ValueError:
raise ValueError(
'Occupation should have a leading dimension that is the same as coeff. '
@@ -270,9 +287,8 @@ def density_grid(
def density_grid_reciprocal(
coeff: Complex[Array, 'spin kpt band x y z'],
vol: Union[float, Array],
- occupation: Optional[Float[Array, 'spin kpt band']] = None
-) -> Union[Complex[Array, 'spin kpt band x y z'],
- Complex[Array, 'spin kpt band']]:
+ occupation: Optional[Float[Array, 'spin kpt band']] = None,
+) -> Union[Complex[Array, 'spin kpt band x y z'], Complex[Array, 'spin x y z']]:
r'''Fourier transform of the density grid.
In a system with electrons, the density of the electron is the result of
@@ -311,8 +327,8 @@ def density_grid_reciprocal(
:py:func:`density_grid`.
Returns:
- A complex valued tensor representing :math:`\tilde{\rho}(G)` evaluated at :math:`G`
- generated from :py:func:`jrystal.grid.g_vectors`.
+ A complex valued tensor representing :math:`\tilde{\rho}(G)` evaluated at
+ :math:`G` generated from :py:func:`jrystal.grid.g_vectors`.
'''
dens = density_grid(coeff, vol, occupation)
return jnp.fft.fftn(dens, axes=range(-3, 0))
@@ -330,7 +346,7 @@ def wave_r(
.. math::
- \psi(r) = \frac{1}{\sqrt{\Omega_\text{cell}}} \sum_G c_{G} e^{iGr}
+ \psi(r) = \frac{1}{\sqrt{\Omega_\text{cell}}} \sum_G c_{G} e^{i\vb{G}^\top \vb{r}}
The :code:`coeff` provided is the :math:`c_{G}`, the :code:`cell_vectors` is
used to generate the grid of frequency components :math:`G` by calling the function
diff --git a/jrystal/_src/pw_test.py b/jrystal/_src/pw_test.py
index 1ea8cbc..0d372dc 100644
--- a/jrystal/_src/pw_test.py
+++ b/jrystal/_src/pw_test.py
@@ -14,7 +14,6 @@
import jax
import jax.numpy as jnp
-
import jrystal as jr
import numpy as np
from absl.testing import absltest, parameterized
diff --git a/jrystal/_src/spmd/__init__.py b/jrystal/_src/spmd/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/jrystal/_src/spmd/custom_partitioning.py b/jrystal/_src/spmd/custom_partitioning.py
new file mode 100644
index 0000000..1cde41a
--- /dev/null
+++ b/jrystal/_src/spmd/custom_partitioning.py
@@ -0,0 +1,248 @@
+from __future__ import annotations
+
+from functools import partial
+from typing import Any
+
+import jax
+import numpy as np
+from jax import tree_util
+from jax._src import api_util, config, core, custom_api_util, dispatch
+from jax._src import linear_util as lu
+from jax._src import mesh as mesh_lib
+from jax._src import sharding_impls
+from jax._src import xla_bridge as xb
+from jax._src.custom_partitioning import (
+ _check_for_tracers, _custom_partitioning_abstract_eval,
+ _custom_partitioning_impl,
+ _custom_partitioning_infer_sharding_from_operands,
+ _custom_partitioning_partition,
+ _custom_partitioning_propagate_user_sharding, _resolve_kwargs,
+ _sharding_callbacks, _ShardingCallbackInfo)
+from jax._src.interpreters import batching, mlir
+from jax._src.interpreters import partial_eval as pe
+from jax._src.lib import xla_client as xc
+from jax._src.lib.mlir import ir
+from jax._src.lib.mlir.dialects import hlo
+
+_CUSTOM_PARTITIONING_CALL_NAME = "CustomSPMDPartitioningJrystal"
+custom_partitioning_p = core.Primitive("jrystal_custom_partitioning")
+custom_partitioning_p.multiple_results = True
+dispatch.prim_requires_devices_during_lowering.add(custom_partitioning_p)
+custom_partitioning_p.def_abstract_eval(_custom_partitioning_abstract_eval)
+custom_partitioning_p.def_impl(_custom_partitioning_impl)
+
+
+@custom_api_util.register_custom_decorator_type
+class jrystal_custom_partitioning:
+
+ def __init__(self, fun, static_argnums=()):
+ self.fun = fun
+ self.partition = None
+ self.static_argnums = static_argnums
+ self.propagate_user_sharding = None
+ self.infer_sharding_from_operands = None
+
+ __getattr__: Any = custom_api_util.forward_attr
+
+ def def_partition(
+ self,
+ partition,
+ infer_sharding_from_operands,
+ propagate_user_sharding=None,
+ decode_shardings=True
+ ):
+ self.partition = partition
+ self.propagate_user_sharding = propagate_user_sharding
+ self.infer_sharding_from_operands = infer_sharding_from_operands
+ self.decode_shardings = decode_shardings
+ return partition
+
+ def __call__(self, *args, **kwargs):
+ args = _resolve_kwargs(self.fun, args, kwargs)
+ debug = api_util.debug_info(
+ "custom_partitioning",
+ self.fun,
+ args,
+ kwargs,
+ static_argnums=self.static_argnums
+ )
+ if self.static_argnums:
+ static_argnums = set(self.static_argnums)
+ args = tuple(x if i in static_argnums else x for i, x in enumerate(args))
+ dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]
+ f_, dyn_args = api_util.argnums_partial(
+ lu.wrap_init(self.fun, debug_info=debug),
+ dyn_argnums,
+ args,
+ require_static_args_hashable=False,
+ )
+ static_args = [args[i] for i in self.static_argnums]
+ _check_for_tracers(static_args)
+ else:
+ static_args = []
+ f_, dyn_args = lu.wrap_init(self.fun, debug_info=debug), args
+ args_flat, in_tree = tree_util.tree_flatten(dyn_args)
+ flat_fun, out_tree = api_util.flatten_fun_nokwargs(f_, in_tree)
+ in_avals = [core.get_aval(x) for x in args_flat]
+ mesh = mesh_lib.thread_resources.env.physical_mesh
+ with core.extend_axis_env_nd(mesh.shape.items()):
+ jaxpr, _, consts, () = pe.trace_to_jaxpr_dynamic(flat_fun, in_avals)
+ assert not len(consts)
+ closed_call = core.ClosedJaxpr(pe.convert_constvars_jaxpr(jaxpr), ())
+
+ propagate_user_sharding = None
+ infer_sharding_from_operands = None
+ sharding_rule = None
+ if config.use_shardy_partitioner.value:
+ sharding_rule = self.sharding_rule
+ else:
+ propagate_user_sharding = self.propagate_user_sharding
+ infer_sharding_from_operands = self.infer_sharding_from_operands
+
+ out_flat = custom_partitioning_p.bind(
+ *consts,
+ *args_flat,
+ call=closed_call,
+ partition=self.partition,
+ propagate_user_sharding=propagate_user_sharding,
+ infer_sharding_from_operands=infer_sharding_from_operands,
+ decode_shardings=self.decode_shardings,
+ sharding_rule=sharding_rule,
+ in_tree=in_tree,
+ out_tree=out_tree(),
+ static_args=static_args
+ )
+ return tree_util.tree_unflatten(out_tree(), out_flat)
+
+
+def _custom_partitioning_lowering_rule(
+ ctx: mlir.LoweringRuleContext,
+ *values,
+ call,
+ in_tree,
+ out_tree,
+ propagate_user_sharding,
+ partition,
+ infer_sharding_from_operands,
+ decode_shardings,
+ sharding_rule,
+ static_args
+):
+ axis_context = ctx.module_context.axis_context
+ if (
+ isinstance(axis_context, sharding_impls.SPMDAxisContext) and
+ set(axis_context.manual_axes) == set(axis_context.mesh.axis_names)
+ ):
+ return mlir.lower_fun(
+ core.jaxpr_as_fun(call), multiple_results=True
+ )(ctx, *values)
+
+ mesh = mesh_lib.thread_resources.env.physical_mesh
+ if isinstance(axis_context, sharding_impls.ShardingContext):
+ devices = axis_context.device_assignment
+ if devices is None:
+ devices = jax.devices()
+
+ am = axis_context.abstract_mesh
+ if am is not None:
+ mesh = mesh_lib.Mesh(
+ np.array(devices).reshape(am.axis_sizes), am.axis_names
+ )
+ elif isinstance(axis_context, sharding_impls.SPMDAxisContext):
+ devices = axis_context.mesh._flat_devices_tuple
+ else:
+ devices = None
+
+ if not devices or len(devices) == 1:
+ return mlir.lower_fun(
+ core.jaxpr_as_fun(call), multiple_results=True
+ )(ctx, *values)
+
+ def to_mesh_pspec_sharding(hlo_sharding: xc.HloSharding | None, ndim):
+ if hlo_sharding is None:
+ return hlo_sharding
+ if mesh.empty or not decode_shardings:
+ assert devices is not None
+ return sharding_impls._op_sharding_to_pos_sharding(hlo_sharding, devices)
+ pspec = sharding_impls.parse_flatten_op_sharding(hlo_sharding, mesh)[0]
+ pspec = jax.sharding.PartitionSpec(*pspec, *((None,) * (ndim - len(pspec))))
+ return jax.sharding.NamedSharding(mesh, pspec)
+
+ sharding_callback_info = _ShardingCallbackInfo(
+ propagate_user_sharding,
+ partition,
+ to_mesh_pspec_sharding,
+ in_tree,
+ out_tree,
+ infer_sharding_from_operands,
+ ctx.module_context,
+ mesh,
+ static_args
+ )
+ key = str(id(sharding_callback_info))
+ _sharding_callbacks[bytes(key, 'utf8')] = sharding_callback_info
+ # We need to make sure `sharding_callback_info` is still alive when the SPMD
+ # partitioner runs so we keep it alive by attaching it to the executable.
+ ctx.module_context.add_keepalive(sharding_callback_info)
+
+ result_types = [mlir.aval_to_ir_type(s) for s in call.out_avals]
+ out = hlo.CustomCallOp(
+ result_types,
+ list(values),
+ call_target_name=ir.StringAttr.get(_CUSTOM_PARTITIONING_CALL_NAME),
+ has_side_effect=ir.BoolAttr.get(False),
+ api_version=mlir.i32_attr(2),
+ called_computations=ir.ArrayAttr.get([]),
+ backend_config=ir.StringAttr.get(key),
+ operand_layouts=None,
+ result_layouts=None
+ )
+ return out.results
+
+
+mlir.register_lowering(
+ custom_partitioning_p, _custom_partitioning_lowering_rule
+)
+
+xc.register_custom_call_partitioner(
+ _CUSTOM_PARTITIONING_CALL_NAME,
+ _custom_partitioning_propagate_user_sharding,
+ _custom_partitioning_partition,
+ _custom_partitioning_infer_sharding_from_operands,
+ can_side_effecting_have_replicated_sharding=True,
+)
+
+xb.register_plugin_callbacks(
+ partial(
+ xc.register_custom_call_partitioner,
+ name=_CUSTOM_PARTITIONING_CALL_NAME,
+ prop_user_sharding=_custom_partitioning_propagate_user_sharding,
+ partition=_custom_partitioning_partition,
+ infer_sharding_from_operands=_custom_partitioning_infer_sharding_from_operands,
+ can_side_effecting_have_replicated_sharding=True,
+ )
+)
+
+
+def _custom_partitioning_batching_rule(
+ batched_args, batch_dims, *, call, in_tree, out_tree, **params
+):
+ # Move batch dimensions to the front
+ args_flat = []
+ for arg, bdim in zip(batched_args, batch_dims):
+ if bdim is not None:
+ arg = batching.moveaxis(arg, bdim, 0)
+ args_flat.append(arg)
+
+ # Apply the custom partitioning operation
+ out_flat = custom_partitioning_p.bind(
+ *args_flat, call=call, in_tree=in_tree, out_tree=out_tree, **params
+ )
+
+ # Return result with batch dimension at front
+ return out_flat, (0,) * len(out_flat)
+
+
+# Register the batching rule
+batching.primitive_batchers[custom_partitioning_p
+ ] = _custom_partitioning_batching_rule
diff --git a/jrystal/_src/spmd/custom_sharding.py b/jrystal/_src/spmd/custom_sharding.py
new file mode 100644
index 0000000..18e67e2
--- /dev/null
+++ b/jrystal/_src/spmd/custom_sharding.py
@@ -0,0 +1,57 @@
+"""Customized operations that better supports single program, multiple data
+(spmd) computation.
+
+See:
+https://jax.readthedocs.io/en/latest/jax.experimental.custom_partitioning.html
+"""
+
+from typing import Callable
+
+import jax
+from jax.sharding import NamedSharding
+from jax.sharding import PartitionSpec as P
+
+from .custom_partitioning import jrystal_custom_partitioning
+
+
+def _supported_sharding(sharding, shape):
+ rank = len(shape.shape)
+ max_shared_dims = min(len(sharding.spec), rank - 1)
+ names = tuple(sharding.spec[:max_shared_dims]
+ ) + tuple(None for _ in range(rank - max_shared_dims))
+ return NamedSharding(sharding.mesh, P(*names))
+
+
+def _infer_sharding_from_operands(mesh, arg_shapes, result_shape):
+ arg_shardings = jax.tree_map(lambda x: x.sharding, arg_shapes)
+ return _supported_sharding(arg_shardings[0], arg_shapes[0])
+
+
+def custom_sharding_by_mesh(fun: Callable[..., jax.Array]) -> Callable:
+ """Inserts a CustomCallOp into the XLA graph with custom SPMD lowering rules
+ such that the transformed function can be sharded over the first batched
+ dimensions.
+
+ Args:
+ fun (Callable[..., jax.Array]): a function that takes arguments have
+ batched dimensions.
+
+ Returns:
+ Callable:
+ """
+
+ def _partition(mesh, arg_shapes, result_shape):
+ result_shardings = jax.tree_map(lambda x: x.sharding, result_shape)
+ arg_shardings = jax.tree_map(lambda x: x.sharding, arg_shapes)
+ # For FFT operations, we want to maintain the same sharding pattern
+ # for both input and output
+ sharding = _supported_sharding(arg_shardings[0], arg_shapes[0])
+ return mesh, fun, sharding, (sharding,)
+
+ sharded_fun = jrystal_custom_partitioning(fun)
+ sharded_fun.def_partition(
+ infer_sharding_from_operands=_infer_sharding_from_operands,
+ partition=_partition,
+ )
+
+ return sharded_fun
diff --git a/jrystal/_src/spmd/fft.py b/jrystal/_src/spmd/fft.py
new file mode 100644
index 0000000..3c7d610
--- /dev/null
+++ b/jrystal/_src/spmd/fft.py
@@ -0,0 +1,134 @@
+"""Customized FFT operations that better supports single program, multiple data
+(SPMD) computation.
+
+The default jax.fft.fftn function computes the discrete Fourier transform of an
+(n+m)-dimensional input along the last n dimensions, and is batched along the
+first m dimensions. By default, however, it will ignore the sharding of the
+input and gather the input on all devices. We need to utilize the module jax.
+experimental.custom_partitioning to insert a CustomCallOp into the XLA graph
+with custom SPMD lowering results.
+
+Example:
+ >>> import jax.numpy as jnp
+ >>> from jrystal.spmd.fft import fftn3d, ifftn3d
+ >>>
+ >>> # Create a 3D array
+ >>> x = jnp.random.randn(8, 8, 8)
+ >>>
+ >>> # Apply 3D FFT
+ >>> y = fftn3d(x)
+ >>>
+ >>> # Apply inverse 3D FFT
+ >>> z = ifftn3d(y)
+"""
+
+from typing import Any, Tuple
+
+import jax
+import jax.interpreters
+import jax.numpy as jnp
+from jax import core
+from jax._src.interpreters import batching
+from jax.interpreters import mlir
+
+from .custom_sharding import custom_sharding_by_mesh
+
+
+def fftn3d(x: jnp.ndarray) -> jnp.ndarray:
+ """Compute the 3D FFT with proper SPMD support.
+
+ Args:
+ x: Input array of shape (..., x, y, z) where the last 3 dimensions
+ will be transformed.
+
+ Returns:
+ Array of same shape as input containing the 3D FFT.
+ """
+ if x.ndim < 3:
+ raise ValueError(f"Input must have at least 3 dimensions, got {x.ndim}")
+ return _fftn_p.bind(x)
+
+
+def ifftn3d(x: jnp.ndarray) -> jnp.ndarray:
+ """Compute the inverse 3D FFT with proper SPMD support.
+
+ Args:
+ x: Input array of shape (..., N, M, P) where the last 3 dimensions
+ will be transformed.
+
+ Returns:
+ Array of same shape as input containing the inverse 3D FFT.
+ """
+ if x.ndim < 3:
+ raise ValueError(f"Input must have at least 3 dimensions, got {x.ndim}")
+ return _ifftn_p.bind(x)
+
+
+# Core implementation functions
+@custom_sharding_by_mesh
+def _ifftn_impl(x: jnp.ndarray) -> jnp.ndarray:
+ return jnp.fft.ifftn(x, axes=range(-3, 0))
+
+
+@custom_sharding_by_mesh
+def _fftn_impl(x: jnp.ndarray) -> jnp.ndarray:
+ return jnp.fft.fftn(x, axes=range(-3, 0))
+
+
+# Primitive definitions
+_ifftn_p = core.Primitive("ifftn_sharding")
+_ifftn_p.def_impl(_ifftn_impl)
+mlir.register_lowering(_ifftn_p, mlir.lower_fun(_ifftn_impl, False))
+
+_fftn_p = core.Primitive("fftn_sharding")
+_fftn_p.def_impl(_fftn_impl)
+mlir.register_lowering(_fftn_p, mlir.lower_fun(_fftn_impl, False))
+
+
+def _ifftn_jvp(primals, tangents):
+ return ifftn3d(*primals), ifftn3d(*tangents)
+
+
+def _ifftn_tranpose_rule(cotangent, x):
+ return ifftn3d(cotangent),
+
+
+def _fftn_jvp(primals, tangents):
+ return fftn3d(*primals), fftn3d(*tangents)
+
+
+def _fftn_tranpose_rule(cotangent, x):
+ return fftn3d(cotangent),
+
+
+def _ifftn_batching_rule(batched_args, batch_dims):
+ x, = batched_args
+ bd, = batch_dims
+ x = batching.moveaxis(x, bd, 0)
+ return ifftn3d(x), 0
+
+
+def _fftn_batching_rule(batched_args, batch_dims):
+ x, = batched_args
+ bd, = batch_dims
+ x = batching.moveaxis(x, bd, 0)
+ return fftn3d(x), 0
+
+
+@_ifftn_p.def_abstract_eval
+def _ifftn_abstract_eval(x):
+ return core.ShapedArray(x.shape, dtype=x.dtype)
+
+
+@_fftn_p.def_abstract_eval
+def _fftn_abstract_eval(x):
+ return core.ShapedArray(x.shape, dtype=x.dtype)
+
+
+jax._src.interpreters.ad.primitive_jvps[_ifftn_p] = _ifftn_jvp
+jax._src.interpreters.ad.deflinear2(_ifftn_p, _ifftn_tranpose_rule)
+jax._src.interpreters.batching.primitive_batchers[_ifftn_p
+ ] = _ifftn_batching_rule
+jax._src.interpreters.ad.primitive_jvps[_fftn_p] = _fftn_jvp
+jax._src.interpreters.ad.deflinear2(_fftn_p, _fftn_tranpose_rule)
+jax._src.interpreters.batching.primitive_batchers[_fftn_p] = _fftn_batching_rule
diff --git a/jrystal/_src/spmd/uniform.py b/jrystal/_src/spmd/uniform.py
new file mode 100644
index 0000000..47316f4
--- /dev/null
+++ b/jrystal/_src/spmd/uniform.py
@@ -0,0 +1,51 @@
+from typing import Optional
+
+import jax
+import numpy as np
+from jax.sharding import NamedSharding
+from jaxtyping import Array
+
+
+def uniform(
+ key: Array,
+ shape: tuple,
+ out_sharding: Optional[NamedSharding] = None
+) -> Array:
+ if out_sharding is None:
+ return jax.random.uniform(key, shape)
+
+ mesh_shape = list(out_sharding.mesh.shape.values())
+ sharding_dim = len(mesh_shape)
+ num_devices = np.prod(mesh_shape)
+ # assert num_devices == jax.device_count(), f"Number of devices {num_devices} does not match the number of devices in the sharding {jax.device_count()}"
+
+ single_device_arrays_shape = list(shape)
+ for i in range(sharding_dim):
+ single_device_arrays_shape[i] //= mesh_shape[i]
+
+ output = []
+ for i in range(num_devices):
+ key, subkey = jax.random.split(key)
+ u = jax.random.uniform(subkey, single_device_arrays_shape)
+ output.append(jax.device_put(u, jax.devices()[i]))
+
+ return jax.make_array_from_single_device_arrays(shape, out_sharding, output)
+
+
+if __name__ == "__main__":
+ from functools import partial
+
+ import jax
+ import jax.numpy as jnp
+ import numpy as np
+ from jax.experimental.pjit import pjit
+ from jax.sharding import Mesh, NamedSharding
+ from jax.sharding import PartitionSpec as P
+
+ num_gpus = len(jax.local_devices())
+ mesh = Mesh(np.array(jax.devices()).reshape([1, 1, -1]), ('s', 'k', 'i'))
+ spec = P('s', 'k', 'i')
+ named_sharding = NamedSharding(mesh, spec)
+
+ with mesh:
+ uniform(jax.random.PRNGKey(123), (1, 1, 16, 123), named_sharding)
diff --git a/jrystal/_src/unitary_module.py b/jrystal/_src/unitary_module.py
index 201b7d8..c09072d 100644
--- a/jrystal/_src/unitary_module.py
+++ b/jrystal/_src/unitary_module.py
@@ -18,21 +18,28 @@
A unitary matrix is a complex square matrix whose conjugate transpose is equal to its inverse.
For real matrices, this reduces to an orthogonal matrix where the transpose equals the inverse.
"""
+from typing import Dict, List, Optional, Union
+
import jax
import jax.numpy as jnp
+from jax.sharding import Sharding
from jaxtyping import Array
-from typing import Dict, Union, List
+from .spmd.uniform import uniform
-def unitary_matrix(params: Dict[str, Array], complex: bool = False) -> Array:
+def unitary_matrix(
+ params: Dict[str, Array],
+ complex: bool = False,
+ sharding: Optional[Sharding] = None
+) -> Array:
"""Generate a unitary (or orthogonal) matrix from its parameters.
This function constructs a unitary matrix (or orthogonal matrix in the real case) using the QR decomposition method. The resulting matrix will have orthogonal columns and determinant ±1.
-
+
Example:
-
+
.. code-block:: python
-
+
key = jax.random.PRNGKey(0)
# Generate a 3x3 complex unitary matrix
@@ -44,10 +51,10 @@ def unitary_matrix(params: Dict[str, Array], complex: bool = False) -> Array:
Args:
params(Dict[str, Array]): Dictionary containing the matrix parameters with keys:
-
+
- 'w_re': Real part of the weight matrix
- 'w_im': Imaginary part of the weight matrix (used if complex=True)
-
+
params can be generated by :func:`unitary_matrix_param_init`.
complex(bool): If True, generates a complex unitary matrix. If False, generates a real orthogonal matrix. Defaults to False.
@@ -63,14 +70,22 @@ def unitary_matrix(params: Dict[str, Array], complex: bool = False) -> Array:
weight_imaginary = 0.
weight = weight_real + weight_imaginary
- orthogonal_columns = jnp.linalg.qr(weight, mode='reduced')[0]
+
+ def _qr(weight):
+ return jnp.linalg.qr(weight, mode='reduced')[0]
+
+ if sharding:
+ _qr = jax.jit(_qr, in_shardings=sharding)
+
+ orthogonal_columns = _qr(weight)
return orthogonal_columns
def unitary_matrix_param_init(
key: Array,
shape: Union[tuple, List[int]],
- complex: bool = True
+ complex: bool = True,
+ sharding: Optional[Sharding] = None
) -> Dict[str, Array]:
"""Initialize parameters for generating a unitary matrix.
@@ -79,14 +94,14 @@ def unitary_matrix_param_init(
using uniform random distributions.
Example:
-
+
.. code-block:: python
-
+
key = jax.random.PRNGKey(42)
params = unitary_matrix_param_init(key, (2, 2))
sorted(params.keys())
>>> ['w_im', 'w_re']
-
+
Args:
key (Array): JAX PRNG key for random number generation.
shape (tuple or List[int]): Tuple of ints specifying the dimensions of the matrix.
@@ -94,16 +109,18 @@ def unitary_matrix_param_init(
Returns:
dict: Dictionary containing:
-
+
- 'w_re': Real weights of shape `shape`
- 'w_im': Imaginary weights of shape `shape` (if complex=True)
"""
key_re, key_im = jax.random.split(key)
- weight_real = jax.random.uniform(key_re, shape)
+ weight_real = uniform(key_re, shape, out_sharding=sharding)
+
if complex:
- weight_imaginary = jax.random.uniform(key_im, shape)
+ weight_imaginary = uniform(key_im, shape, out_sharding=sharding)
else:
weight_imaginary = 0.
+
return {'w_re': weight_real, 'w_im': weight_imaginary}
diff --git a/jrystal/_src/utils.py b/jrystal/_src/utils.py
index 4bd590d..9eea735 100644
--- a/jrystal/_src/utils.py
+++ b/jrystal/_src/utils.py
@@ -43,12 +43,12 @@ def safe_real(array: Array, tol: float = 1e-8) -> Array:
specified tolerance.
Example:
-
+
.. code-block:: python
-
+
x = 1.0 + 1e-10j
safe_real(x) # Returns 1.0
- y = 1.0 + 1.0j
+ y = 1.0 + 1.0j
safe_real(y) # Raises ValueError
"""
if jnp.iscomplexobj(array):
@@ -76,9 +76,9 @@ def vmapstack(times: int, args: List[Dict] = None) -> Callable:
vmap applications (times).
Example:
-
+
.. code-block:: python
-
+
@vmapstack(2)
def f(x):
return x * 2
@@ -109,14 +109,14 @@ def absolute_square(array: Complex[Array, '...']) -> Float[Array, '...']:
"""Computes the squared magnitude of complex numbers in an array.
Calculates :math:`|z|^2` for each complex number :math:`z` in the input array by multiplying each element with its complex conjugate. This operation preserves the array shape while converting complex values to their real squared magnitudes.
-
+
.. note::
This is equivalent to :math:`(Re(z))^2 + (Im(z))^2` for each complex number :math:`z`, but is computed using complex conjugate multiplication for better numerical stability.
Example:
-
+
.. code-block:: python
-
+
x = 3 + 4j
absolute_square(x) # Returns 25.0 (|3 + 4j|² = 3² + 4² = 25)
@@ -140,9 +140,9 @@ def volume(cell_vectors: Float[Array, '3 3']) -> Float:
The volume is calculated as :math:`|det(A)|` where :math:`A` is the matrix of cell vectors. This gives the volume of the parallelepiped formed by the three vectors regardless of their orientation.
Example:
-
+
.. code-block:: python
-
+
# For a cubic cell of side length 2
vectors = jnp.array([[2., 0., 0.],
[0., 2., 0.],
@@ -161,30 +161,34 @@ def volume(cell_vectors: Float[Array, '3 3']) -> Float:
def wave_to_density(
wave_grid: Complex[Array, 'spin kpt band x y z'],
occupation: Optional[Float[Array, 'spin kpt band']] = None,
- axis: Optional[Union[int, Tuple, List]] = None
-) -> Union[Float[Array, 'spin kpt band x y z'], Float[Array, 'spin kpt band']]:
+) -> Union[Float[Array, 'spin x y z'], Float[Array, 'spin kpt band x y z']]:
"""Computes electron density from wave functions in real space.
Calculates the electron density by taking the absolute square of wave functions and optionally applying occupation numbers. The density can be computed for the full grid or reduced along specified dimensions.
Args:
- wave_grid (Complex[Array, 'spin kpt band x y z']): Complex wave function values on a real-space grid. The array has dimensions for spin, k-points, bands, and spatial coordinates (x,y,z).
- occupation (Optional[Float[Array, 'spin kpt band']]): Optional occupation numbers for each state (spin, k-point, band). If provided, the density will be weighted by these values. Defaults to None.
- axis (Optional[Union[int, Tuple, List]]): Optional specification of axes along which to sum the density when applying occupation numbers. If None, sums over spin, k-points, and bands (first three dimensions). Defaults to None.
+ wave_grid (Complex[Array, 'spin kpt band x y z']): Complex wave function
+ values on a real-space grid. The array has dimensions for spin,
+ k-points, bands, and spatial coordinates (x,y,z).
+ occupation (Optional[Float[Array, 'spin kpt band']]): Optional occupation
+ numbers for each state (spin, k-point, band). If provided, the density
+ will be weighted by these values. Defaults to None.
Returns:
- Union[Float[Array, 'spin kpt band x y z'], Float[Array, 'spin kpt band']]: The electron density grid. If occupation is None, the density grid has the same shape as the input wave_grid. If occupation is provided, the density grid is reduced along the specified axes after applying occupation weights.
+ Union[Float[Array, 'spin x y z'], Float[Array, 'spin kpt band x y z']]:
+ The electron density grid. If occupation is None, the density grid has
+ the same shape as the input wave_grid. If occupation is provided, the
+ density grid is reduced over the k-points and bands dimensions.
Raises:
ValueError: If the shapes of wave_grid and occupation are incompatible for broadcasting.
"""
dens = absolute_square(wave_grid)
- axis = range(0, wave_grid.ndim - 3) if axis is None else axis
if occupation is not None:
try:
occupation = jnp.expand_dims(occupation, range(-3, 0))
- dens = jnp.sum(dens * occupation, axis=axis)
+ dens = jnp.sum(dens * occupation, axis=(1, 2))
except:
raise ValueError(
f"wave_grid's shape ({wave_grid.shape}) and occupation's shape "
@@ -196,8 +200,7 @@ def wave_to_density(
def wave_to_density_reciprocal(
wave_grid: Complex[Array, 'spin kpt band x y z'],
occupation: Optional[Float[Array, 'spin kpt band']] = None,
- axis: Optional[Union[int, Tuple, List]] = None
-) -> Union[Float[Array, 'spin kpt band x y z'], Float[Array, 'spin kpt band']]:
+) -> Union[Float[Array, 'spin x y z'], Float[Array, 'spin kpt band x y z']]:
"""Computes electron density from wave functions in reciprocal space.
Calculates the electron density by first computing the real-space density
@@ -206,15 +209,18 @@ def wave_to_density_reciprocal(
reciprocal space, such as computing the Hartree potential.
Args:
- wave_grid (Complex[Array, 'spin kpt band x y z']): Complex wave function values on a real-space grid. The array has dimensions for spin, k-points, bands, and spatial coordinates (x,y,z).
- occupation (Optional[Float[Array, 'spin kpt band']]): Optional occupation numbers for each state (spin, k-point, band). If provided, the density will be weighted by these values. Defaults to None.
- axis (Optional[Union[int, Tuple, List]]): Optional specification of axes along which to sum the density when applying occupation numbers. If None, sums over spin, k-points, and bands (first three dimensions). Defaults to None.
+ wave_grid (Complex[Array, 'spin kpt band x y z']): Complex wave
+ function values on a real-space grid. The array has dimensions for
+ spin, k-points, bands, and spatial coordinates (x,y,z).
+ occupation (Optional[Float[Array, 'spin kpt band']]): Optional
+ occupation numbers for each state (spin, k-point, band). If provided,
+ the density will be weighted by these values. Defaults to None.
Returns:
- Union[Float[Array, 'spin kpt band x y z'], Float[Array, 'spin kpt band']]: The electron density grid in reciprocal space. If occupation is None, the density grid has the same shape as the input wave_grid. If occupation is provided, the density grid is reduced along the specified axes after applying occupation weights.
+ Union[Float[Array, 'spin kpt band x y z'], Float[Array, 'spin kpt band']]: The electron density grid in reciprocal space. If occupation is None, the density grid has the same shape as the input wave_grid. If occupation is provided, the density grid is reduced over the k-points and bands dimensions.
"""
- dens = wave_to_density(wave_grid, occupation, axis)
+ dens = wave_to_density(wave_grid, occupation)
return jnp.fft.fftn(dens, axes=range(-3, 0))
@@ -222,12 +228,12 @@ def fft_factor(n: int) -> int:
"""Finds the smallest valid FFT size that is >= n.
Determines the smallest number greater than or equal to n that can be
- factored as
-
+ factored as
+
.. math::
-
+
\\text{FFT size} = 2^a \\times 3^b \\times 5^c \\times 7^d \\times 11^e \\times 13^f
-
+
where :math:`e` and :math:`f` are either 0 or 1.
Args:
@@ -264,7 +270,7 @@ def expand_coefficient(
Complex[Array, "spin kpt band x y z"]: The expanded coefficient array with dimensions matching the batch dimensions of coeff_compact (spin, kpt, band) followed by the spatial dimensions of the mask (x, y, z).
.. note::
-
+
The function first swaps the last two axes of the input coefficients to align with the expected output format, then creates a zero-filled array of the target shape and places the coefficients at the masked positions.
"""
@@ -287,7 +293,7 @@ def squeeze_coefficient(
efficient computation.
.. note::
-
+
The function extracts values at masked positions and then swaps the last two axes to arrange the output as (spin, kpt, gpt, band) rather than (spin, kpt, band, gpt).
Args:
diff --git a/jrystal/_src/xc.py b/jrystal/_src/xc.py
new file mode 100644
index 0000000..79e7f6f
--- /dev/null
+++ b/jrystal/_src/xc.py
@@ -0,0 +1,273 @@
+import importlib
+from typing import Callable, Optional
+
+import jax
+import jax.numpy as jnp
+from jax.lax import stop_gradient
+from jax_xc.utils import get_p
+from jaxtyping import Array, Float
+
+from .utils import absolute_square, safe_real
+
+
+def get_xc_functional(xc: str = 'gga_x_pbe', polarized: bool = False):
+ """Dynamically import XC functional implementation.
+
+ Args:
+ functional_type: String like 'pbe', 'b3lyp', etc.
+ polarization: String, either 'unpol' or 'pol' for unpolarized/polarized
+
+ Returns:
+ The requested functional implementation
+ """
+ polarization = 'pol' if polarized else 'unpol'
+ try:
+ module_path = f"jax_xc.impl.{xc}"
+ module = importlib.import_module(module_path)
+ functional = getattr(module, polarization)
+ return functional
+ except (ImportError, AttributeError) as e:
+ raise ImportError(
+ f"Could not import {polarization} from {module_path}: {e}"
+ )
+
+
+# Constants in libxc
+XC_UNPOLARIZED = 0
+XC_POLARIZED = 1
+
+
+def _parse_xc_type(xc_type: str, rho_r: Float[Array, 's x y z']):
+ polarized = rho_r.ndim > 0 and rho_r.shape[0] == 2
+ is_exchange = '_x' in xc_type # HACK
+ if is_exchange: # NOTE: libxc's polarized exchange functionals might be problematic
+ p = get_p(xc_type, XC_UNPOLARIZED)
+ f = get_xc_functional(xc_type, polarized=False)
+ else:
+ p = get_p(xc_type, XC_POLARIZED if polarized else XC_UNPOLARIZED)
+ f = get_xc_functional(xc_type, polarized=polarized)
+ grid_shape = rho_r.shape[1:]
+ rho_r_flat = rho_r.reshape(2 if polarized else 1, -1)
+ return polarized, is_exchange, p, f, grid_shape, rho_r_flat
+
+
+def _lda(xc_type: str, rho_r: Float[Array, 's x y z']):
+ polarized, is_exchange, p, f, grid_shape, rho_r_flat = _parse_xc_type(xc_type, rho_r)
+ if polarized and is_exchange: # NOTE: exact scaling relation
+ f_up = jax.vmap(f, (None, 0), 0)(p, 2 * rho_r_flat[0])
+ f_dn = jax.vmap(f, (None, 0), 0)(p, 2 * rho_r_flat[1])
+ f_val = 0.5 * (f_up + f_dn)
+ elif polarized: # and is correlation
+ f_val = jax.vmap(f, (None, 1), 0)(p, rho_r_flat)
+ else: # not polarized
+ f_val = jax.vmap(f, (None, 0), 0)(p, rho_r_flat[0])
+ return f_val.reshape(grid_shape)
+
+
+def _gga(
+ xc_type: str,
+ rho_r: Float[Array, 's x y z'],
+ sigma_r: Float[Array, 's x y z']
+):
+ polarized, is_exchange, p, f, grid_shape, rho_r_flat = _parse_xc_type(xc_type, rho_r)
+ sigma_r_flat = sigma_r.reshape(3 if polarized else 1, -1)
+ if polarized and is_exchange: # NOTE: exact scaling relation
+ f_up = jax.vmap(f, (None, 0, 0),
+ 0)(p, 2 * rho_r_flat[0], 4 * sigma_r_flat[0])
+ # NOTE: sigma_r_flat[1] is the cross term
+ f_dn = jax.vmap(f, (None, 0, 0),
+ 0)(p, 2 * rho_r_flat[1], 4 * sigma_r_flat[2])
+ f_val = 0.5 * (f_up + f_dn)
+ elif polarized: # and is correlation
+ f_val = jax.vmap(f, (None, 1, 1), 0)(p, rho_r_flat, sigma_r_flat)
+ else: # not polarized
+ f_val = jax.vmap(f, (None, 0, 0), 0)(p, rho_r_flat[0], sigma_r_flat[0])
+ return f_val.reshape(grid_shape)
+
+
+def sigma_r_fn(
+ density_grid: Float[Array, 's x y z'], gs: Float[Array, 'x y z 3']
+) -> Float[Array, 's x y z']:
+ """Calculate the gradient norms of the density.
+ If polarized, s in the return shape is 3, else 1, as for polarized density
+ we compute the gradient norm of up and down spin, and the cross term."""
+ polarized = density_grid.shape[0] == 2
+ rho_G = jnp.fft.fftn(density_grid, axes=range(-3, 0))
+ grad_rho_G = rho_G[..., None] * 1j * gs
+ grad_rho_r = jnp.fft.ifftn(grad_rho_G, axes=range(-4, -1))
+ if polarized:
+ s_up, s_dn = grad_rho_r
+ return jnp.stack([
+ s_up.conj() * s_up,
+ s_up.conj() * s_dn,
+ s_dn.conj() * s_dn,
+ ]).sum(-1).real
+ else:
+ return absolute_square(grad_rho_r).sum(-1)
+
+
+def vxc_lda(exc: Callable, rho_r: Float[Array, 's x y z']):
+ polarized = rho_r.shape[0] == 2
+ grid_sizes = rho_r.shape[1:]
+
+ rho_r_flat = rho_r.reshape(rho_r.shape[0], -1)
+ if polarized:
+ dexc_drho_flat = jax.vmap(jax.jacfwd(exc), 1, 1)(rho_r_flat)
+ t1 = dexc_drho_flat.reshape(2, *grid_sizes) * rho_r
+ else:
+ dexc_drho_flat = jax.vmap(jax.grad(exc))(rho_r_flat[0])
+ t1 = dexc_drho_flat.reshape(1, *grid_sizes) * rho_r
+
+ t2 = exc(rho_r)
+ vxc_r = t1 + t2
+
+ return vxc_r
+
+
+def vxc_gga_recp(
+ exc: Callable,
+ rho_r: Float[Array, 's x y z'],
+ sigma_r: Float[Array, 's x y z'],
+ gs: Float[Array, 'x y z 3']
+):
+ """Calculate the function derivative of the XC functional in the
+ reciprocal space.
+
+ Note that the correlation part are treated differently
+
+ Args:
+ sigma_r: gradient norm of the density, evaluated on the grid
+ """
+ polarized = rho_r.shape[0] == 2
+ grid_sizes = rho_r.shape[1:]
+
+ # local term
+ rho_r_flat = rho_r.reshape(rho_r.shape[0], -1)
+ sigma_r_flat = sigma_r.reshape(sigma_r.shape[0], -1)
+ if polarized:
+ dexc_drho_flat = jax.vmap(jax.jacfwd(exc), (1, 1),
+ 1)(rho_r_flat, sigma_r_flat)
+ t1 = dexc_drho_flat.reshape(2, *grid_sizes) * rho_r
+ else:
+ dexc_drho_flat = jax.vmap(jax.grad(exc))(rho_r_flat[0], sigma_r_flat[0])
+ t1 = dexc_drho_flat.reshape(1, *grid_sizes) * rho_r
+
+ t2 = exc(rho_r, sigma_r)
+ local_term = t1 + t2
+
+ # gradient correction
+ axes = list(range(-3, 0))
+ rho_G: Float[Array, 's x y z'] = jnp.fft.fftn(rho_r, axes=axes)
+ grad_rho_r = jnp.fft.ifftn(rho_G[..., None] * 1j * gs, axes=axes)
+ lapl_rho_G = -1 * (gs**2).sum(-1) * rho_G
+ lapl_rho_r: Float[Array, 's x y z'] = jnp.fft.ifftn(lapl_rho_G, axes=axes)
+
+ grad_fft = lambda x: jnp.fft.ifftn(1j * gs * jnp.fft.fft(x)[..., None], axes=axes)
+
+ if polarized:
+ dexc_dsigma_flat: Float[Array, '3 num_g']
+ dexc_dsigma_flat = jax.vmap(jax.jacfwd(exc, argnums=1), (1, 1),
+ 1)(rho_r_flat, sigma_r_flat)
+
+ t_up = dexc_dsigma_flat[0].reshape(grid_sizes) * rho_r[0]
+ t_up = jnp.where(sigma_r[0] > 0, t_up, 0)
+ t3_up = (grad_fft(t_up) * grad_rho_r[0]).sum(-1)
+ t4_up = t_up * lapl_rho_r[0]
+
+ t_cross_up = dexc_dsigma_flat[1].reshape(grid_sizes) * rho_r[1]
+ t5_up = (grad_fft(t_cross_up) * grad_rho_r[1]).sum(-1)
+ t6_up = t_cross_up * lapl_rho_r[1]
+
+ grad_correction_up = 2 * (t3_up + t4_up) + t5_up + t6_up
+
+ t_dn = dexc_dsigma_flat[2].reshape(grid_sizes) * rho_r[1]
+ t_dn = jnp.where(sigma_r[2] > 0, t_dn, 0)
+ t3_dn = (grad_fft(t_dn) * grad_rho_r[1]).sum(-1)
+ t4_dn = t_dn * lapl_rho_r[1]
+
+ t_cross_dn = dexc_dsigma_flat[1].reshape(grid_sizes) * rho_r[0]
+ t5_dn = (grad_fft(t_cross_dn) * grad_rho_r[0]).sum(-1)
+ t6_dn = t_cross_dn * lapl_rho_r[0]
+
+ grad_correction_dn = 2 * (t3_dn + t4_dn) + t5_dn + t6_dn
+
+ grad_correction = jnp.stack([grad_correction_up, grad_correction_dn])
+
+ else:
+ dexc_dsigma_flat: Float[Array, 'num_g']
+ dexc_dsigma_flat = jax.vmap(jax.grad(exc, argnums=1)
+ )(rho_r_flat[0], sigma_r_flat[0])
+ t = dexc_dsigma_flat.reshape(grid_sizes) * rho_r[0]
+ t = jnp.where(sigma_r[0] > 0, t, 0)
+ t3 = (grad_fft(t) * grad_rho_r[0]).sum(-1)
+ t4 = t * lapl_rho_r
+ grad_correction = 2 * (t3 + t4)
+
+ integrand = local_term - grad_correction
+
+ vxc_G = jnp.fft.fftn(integrand, axes=axes)
+
+ return vxc_G
+
+
+def vxc_gga(
+ exc: Callable,
+ rho_r: Float[Array, 'x y z'],
+ sigma_r: Float[Array, 'x y z'],
+ gs: Float[Array, 'x y z 3']
+):
+ return jnp.fft.ifftn(vxc_gga_recp(exc, rho_r, sigma_r, gs), axes=list(range(-3, 0)))
+
+
+def xc_density(
+ density_grid: Float[Array, 's x y z'],
+ g_vector_grid,
+ kohn_sham: bool = False,
+ xc_type: str = "lda_x"
+):
+ if xc_type == "lda_x":
+ return _lda_x(density_grid, kohn_sham)
+
+ if "gga" in xc_type:
+ exc_fn = lambda density, grad: sum(
+ [_gga(xc_type_, density, grad) for xc_type_ in xc_type.split('+')]
+ )
+ grad = sigma_r_fn(density_grid, g_vector_grid)
+
+ if kohn_sham:
+ density_grid = stop_gradient(density_grid)
+ vxc_r = vxc_gga(exc_fn, density_grid, grad, g_vector_grid)
+ return vxc_r
+
+ else:
+ return exc_fn(density_grid, grad)
+
+ else: # LDA
+ exc_fn = lambda density: sum(
+ [_lda(xc_type_, density) for xc_type_ in xc_type.split('+')]
+ )
+
+ if kohn_sham:
+ density_grid = stop_gradient(density_grid)
+ vxc_r = vxc_lda(exc_fn, density_grid)
+ return vxc_r
+
+ else:
+ return exc_fn(density_grid)
+
+
+def _lda_x(
+ density_grid: Float[Array, 's x y z'],
+ kohn_sham: bool = False
+):
+ dim = density_grid.ndim
+ if dim > 3:
+ density_grid = jnp.sum(density_grid, axis=range(0, dim - 3))
+
+ if kohn_sham:
+ density_grid = stop_gradient(density_grid)
+ output = -(density_grid * 3. / jnp.pi)**(1 / 3)
+ else:
+ output = -(density_grid * 3. / jnp.pi)**(1 / 3) * 0.75
+
+ return output
diff --git a/jrystal/calc/__init__.py b/jrystal/calc/__init__.py
index e043769..31c4d03 100644
--- a/jrystal/calc/__init__.py
+++ b/jrystal/calc/__init__.py
@@ -14,14 +14,14 @@
"""Calculation module for band structure and energy."""
# from .calc_band_structure import calc as band
-from .calc_ground_state_energy import calc as energy
-from .calc_band_structure import calc as band
+from .calc_ground_state_energy_all_electrons import calc as energy_all_electrons
+from .calc_band_structure_all_electrons import calc as band_all_electrons
from .calc_ground_state_energy_normcons import calc as energy_normcons
from .calc_band_structure_normcons import calc as band_normcons
__all__ = [
- "band",
- "energy",
+ "band_all_electrons",
+ "energy_all_electrons",
"energy_normcons",
"band_normcons",
]
diff --git a/jrystal/calc/calc_band_structure.py b/jrystal/calc/calc_band_structure.py
deleted file mode 100644
index 6adbf79..0000000
--- a/jrystal/calc/calc_band_structure.py
+++ /dev/null
@@ -1,238 +0,0 @@
-# Copyright 2025 Garena Online Private Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Band Structure Calculator. """
-
-import jax
-import jax.numpy as jnp
-import optax
-from math import ceil
-
-import time
-from typing import Optional
-from dataclasses import dataclass
-from absl import logging
-from tqdm import tqdm
-
-from .calc_ground_state_energy import calc as energy_calc
-from .calc_ground_state_energy import GroundStateEnergyOutput
-from .opt_utils import set_env_params, create_crystal, create_freq_mask
-from .opt_utils import create_grids, create_optimizer
-
-from ..config import JrystalConfigDict
-from .._src import pw, hamiltonian, occupation
-from .._src.band import get_k_path
-from .._src.crystal import Crystal
-
-
-@dataclass
-class BandStructureOutput:
- """Output of the band structure calculation.
-
- Args:
- config (JrystalConfigDict): Configuration for the calculation.
- crystal (Crystal): The crystal object.
- params_pw (dict): Parameters for the plane wave basis.
- ground_state_energy_output (GroundStateEnergyOutput): The output of the ground state energy calculation.
- k_path (jax.Array): The K-path.
- band_structure (jax.Array): The band structure.
- """
- config: JrystalConfigDict
- crystal: Crystal
- params_pw: dict
- ground_state_energy_output: GroundStateEnergyOutput
- k_path: jax.Array
- band_structure: jax.Array
-
-
-def calc(
- config: JrystalConfigDict,
- ground_state_energy_output: Optional[GroundStateEnergyOutput] = None
-) -> BandStructureOutput:
- """Calculate the band structure of a crystal.
-
- Args:
- config (JrystalConfigDict): The configuration for the calculation.
- ground_state_energy_output (Optional[GroundStateEnergyOutput], optional): The output of the ground state energy calculation. Defaults to None.
-
- Returns:
- BandStructureOutput: The band structure output of the crystal.
- """
- if config.use_pseudopotential:
- raise RuntimeError(
- "This calculator does not support pseudopotential. It only supports all electron calculations. For norm-conserving pseudopotential calculations, please use the `calc_band_structure_normcons.calc` function."
- )
-
- set_env_params(config)
- key = jax.random.PRNGKey(config.seed)
- crystal = create_crystal(config)
- g_vec, _, k_vec = create_grids(config)
- freq_mask = create_freq_mask(config)
- xc = config.xc
-
- # generate K-path.
- logging.info("===> Generating K-path...")
- k_path = get_k_path(
- crystal.cell_vectors,
- path=config.k_path_special_points,
- num=config.num_kpoints,
- fractional=False
- )
- logging.info(
- f"{k_path.shape[0]} k-points generated. ({config.k_path_special_points})"
- )
-
- # optimitimize ground state energy if not provided.
- if ground_state_energy_output is None:
- logging.info("===> Starting total energy minimization...")
- start = time.time()
- ground_state_energy_output = energy_calc(config)
- logging.info(
- f" Total energy minimization done. "
- f"Time: {time.time()-start:.3f} "
- )
-
- params_pw_ground_state = ground_state_energy_output.params_pw
- params_occ_ground_state = ground_state_energy_output.params_occ
-
- # Calculate ground state density at grid points.
- def get_occupation(params):
- return occupation.idempotent(
- params, crystal.num_electron, k_vec.shape[0], crystal.spin
- )
-
- coeff_ground_state = pw.coeff(params_pw_ground_state, freq_mask)
- occ_ground_state = get_occupation(params_occ_ground_state)
- ground_state_density_grid = pw.density_grid(
- coeff_ground_state, crystal.vol, occ_ground_state
- )
-
- # Define the objective function for band structure calculation.
- def hamiltonian_trace(params_pw_band, kpts):
- coeff_band = pw.coeff(params_pw_band, freq_mask)
- energy = hamiltonian.hamiltonian_matrix_trace(
- coeff_band,
- crystal.positions,
- crystal.charges,
- ground_state_density_grid,
- g_vec,
- kpts,
- crystal.vol
- )
- return jnp.sum(energy).real
-
- # Initialize parameters and optimizer.
- optimizer = create_optimizer(config)
- num_bands = ceil(crystal.num_electron / 2) + config.band_structure_empty_bands
- params_pw_band = pw.param_init(key, num_bands, 1, freq_mask)
- opt_state = optimizer.init(params_pw_band)
-
- # define update function
- @jax.jit
- def update(params, opt_state, kpts):
- hamil_trace, grad = jax.value_and_grad(hamiltonian_trace)(params, kpts)
-
- updates, opt_state = optimizer.update(grad, opt_state)
- params = optax.apply_updates(params, updates)
-
- return params, opt_state, hamil_trace
-
- # the main loop for band structure calculation.
- logging.info("===> Starting band structure calculation...")
- logging.info(f"Number of bands: {num_bands}")
- logging.info("Optimizing the first K point...")
-
- params_kpoint_list = []
- eigen_values = []
-
- if config.verbose:
- iters = tqdm(range(config.band_structure_epoch))
- else:
- iters = tqdm(range(config.band_structure_epoch), disable=True)
-
- start = time.time()
- for i in iters:
- params_pw_band, opt_state, hamil_trace = update(
- params_pw_band, opt_state, k_path[0:1]
- )
- iters.set_description(f"Hamiltonian trace: {hamil_trace:.4E}")
-
- # TODO: introduce convergence tracker.
- converged = True
-
- params_kpoint_list.append(params_pw_band)
- logging.info(f" Converged: {converged}.")
- logging.info(f" Total epochs run: {i+1}.")
- logging.info(f" Training Time: {(time.time() - start):.3f}s.")
- logging.info("===> Starting fine-tuning the rest k points...")
- logging.info(f" fine tuning steps: {config.k_path_fine_tuning_epoch}")
-
- if config.verbose:
- iters = tqdm(range(1, k_path.shape[0]))
- else:
- iters = tqdm(range(1, k_path.shape[0]), disable=True)
-
- for i in iters:
- for _ in range(config.k_path_fine_tuning_epoch):
- params_pw_band, opt_state, hamil_trace = update(
- params_pw_band, opt_state, k_path[i:(i+1)]
- )
- iters.set_description(f" Loss(the {i+1}th k point): {hamil_trace:.4E}")
- params_kpoint_list.append(params_pw_band)
-
- logging.info("===> Band structure calculation done.")
-
- ########################################################
- # One-time eigen decomposition
- logging.info("===> Diagonalizing the Hamiltonian matrix...")
-
- @jax.jit
- def eig_fn(param, k):
- coeff_k = pw.coeff(param, freq_mask)
- hamil_matrix = hamiltonian.hamiltonian_matrix(
- coeff_k,
- crystal.positions,
- crystal.charges,
- ground_state_density_grid,
- g_vec,
- k,
- crystal.vol,
- xc,
- kohn_sham=True,
- )
-
- eigen_values = jnp.linalg.eigvalsh(hamil_matrix[0])
- return eigen_values
-
- iters = tqdm(range(len(params_kpoint_list)))
- for i in iters:
- prm = params_kpoint_list[i]
- k = k_path[i:(i + 1), :]
- iters.set_description(f"Diagonolizing the {i+1}th k points")
- eig = eig_fn(prm, k)
- eigen_values.append(eig)
-
- # eigen_values = jnp.vstack(eigen_values)
- logging.info("===> Eigen decomposition done.")
- save_file = ''.join(crystal.symbol) + "_band_structure.npy"
- logging.info(f"Results saved in {save_file}")
- jnp.save(save_file, eigen_values)
-
- return BandStructureOutput(
- config=config,
- crystal=crystal,
- params_pw=params_pw_band,
- ground_state_energy_output=ground_state_energy_output,
- k_path=k_path,
- band_structure=eigen_values,
- )
diff --git a/jrystal/calc/calc_band_structure_all_electrons.py b/jrystal/calc/calc_band_structure_all_electrons.py
new file mode 100644
index 0000000..8219b86
--- /dev/null
+++ b/jrystal/calc/calc_band_structure_all_electrons.py
@@ -0,0 +1,226 @@
+# Copyright 2025 Garena Online Private Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Band Structure Calculator. """
+import time
+from dataclasses import dataclass
+from functools import partial
+from math import ceil
+from typing import Optional
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+from absl import logging
+
+from .._src import pw, hamiltonian, energy, potential
+from .._src.band import get_k_path
+from .._src.crystal import Crystal
+from ..config import JrystalConfigDict
+from .calc_ground_state_energy_all_electrons import calc as energy_calc
+from .opt_utils import (
+ create_crystal,
+ create_freq_mask,
+ create_grids,
+ create_optimizer,
+ set_env_params,
+)
+
+# @dataclass
+# class BandStructureOutput:
+# """Output of the band structure calculation.
+
+# Args:
+# config (JrystalConfigDict): The configuration for the calculation.
+# crystal (Crystal): The crystal object.
+# params_pw (dict): Parameters for the plane wave basis.
+# ground_state_energy_output (GroundStateEnergyOutput): The output of the ground state energy calculation.
+# k_path (jax.Array): The K-path.
+# band_structure (jax.Array): The band structure.
+# """
+# config: JrystalConfigDict
+# crystal: Crystal
+# params_pw: dict
+# ground_state_energy_output: GroundStateEnergyOutput
+# k_path: jax.Array
+# band_structure: jax.Array
+
+
+def calc(config: JrystalConfigDict):
+ """Calculate the band structure of a crystal with norm-conserving pseudopotential.
+
+ Args:
+ config (JrystalConfigDict): The configuration for the calculation.
+
+ Returns:
+ BandStructureOutput: The band structure output of the crystal.
+ """
+ set_env_params(config)
+ key = jax.random.PRNGKey(config.seed)
+ crystal = create_crystal(config)
+ num_electrons = crystal.num_electron
+ g_vec, r_vec, k_vec = create_grids(config)
+ freq_mask = create_freq_mask(config)
+ xc = config.xc
+
+ # generate K-path.
+ logging.info("===> Generating K-path...")
+ k_path = get_k_path(
+ crystal.cell_vectors,
+ path=config.k_path_special_points,
+ num=config.num_kpoints,
+ fractional=False
+ )
+ logging.info(f"{k_path.shape[0]} k-points generated.")
+
+ # optimitimize ground state energy if not provided.
+ logging.info("===> Starting total energy minimization...")
+ ground_state_density_grid = energy_calc(config)
+ jax.clear_caches()
+
+ def hamiltonian_trace(params_pw_band, kpts, g_vector_grid):
+ coeff_band = pw.coeff(params_pw_band, freq_mask)
+ output = hamiltonian.hamiltonian_matrix_trace(
+ coeff_band,
+ crystal.positions,
+ crystal.charges,
+ ground_state_density_grid,
+ g_vector_grid,
+ kpts,
+ crystal.vol,
+ xc,
+ kohn_sham=True
+ )
+ return jnp.sum(output)
+
+ # Initialize parameters and optimizer.
+ optimizer = create_optimizer(config)
+ num_bands = ceil(num_electrons / 2) + config.band_structure_empty_bands
+ params_pw_band = pw.param_init(
+ key, num_bands, 1, freq_mask, spin_restricted=config.spin_restricted
+ )
+ opt_state = optimizer.init(params_pw_band)
+
+ # define update function
+ @jax.jit
+ def update(params, opt_state, kpts, g_vector_grid):
+ hamil_trace, grad = jax.value_and_grad(hamiltonian_trace)(
+ params, kpts, g_vector_grid
+ )
+
+ updates, opt_state = optimizer.update(grad, opt_state)
+ params = optax.apply_updates(params, updates)
+
+ return params, opt_state, hamil_trace
+
+ # the main loop for band structure calculation.
+ logging.info("===> Starting band structure calculation...")
+ logging.info(f"Number of bands: {num_bands}")
+ logging.info("Optimizing the first K point...")
+
+ @partial(jax.pmap, in_axes=(0, 0, 0), devices=jax.devices())
+ def optimize_eigenvalues(kpts, params_pw_band, opt_state):
+ logging.info("===> Optimizing the first K point...")
+ eigen_values = []
+
+ def update_scan(carry, xs):
+ params_pw_band, opt_state, kpts = carry
+ params_pw_band, opt_state, hamil_trace = update(
+ params_pw_band, opt_state, kpts, g_vec
+ )
+ return (params_pw_band, opt_state, kpts), None
+
+ carry, _ = jax.lax.scan(
+ update_scan, (params_pw_band, opt_state, kpts[0:1]),
+ length=config.band_structure_epoch, unroll = 1
+ )
+ params_first_kpt, opt_state, _ = carry
+
+ logging.info("===> Fine-tuning the rest K points...")
+
+ def finetuning(carry, x):
+ kpts = x
+ kpts = jnp.expand_dims(kpts, axis=0)
+
+ params_pw_band, opt_state = carry
+
+ carry, _ = jax.lax.scan(
+ update_scan, (params_pw_band, opt_state, kpts),
+ length=config.k_path_fine_tuning_epoch, unroll = 1
+ )
+ params_pw_band, opt_state, _ = carry
+
+ return (params_pw_band, opt_state), params_pw_band
+
+ carry, params_fine_tuning = jax.lax.scan(
+ finetuning, (params_first_kpt, opt_state),
+ xs = (kpts[1:])
+ )
+
+ logging.info("===> Eigen decomposition...")
+
+ def eig_fn(param, kpts, g_vector_grid):
+ coeff_i = pw.coeff(param, freq_mask)
+ hamil_matrix = hamiltonian.hamiltonian_matrix(
+ coeff_i,
+ crystal.positions,
+ crystal.charges,
+ ground_state_density_grid,
+ g_vector_grid,
+ kpts,
+ crystal.vol,
+ xc,
+ kohn_sham=True
+ )
+ return jax.vmap(jnp.linalg.eigvalsh)(hamil_matrix)
+
+ eigen_values = [eig_fn(params_first_kpt, kpts[0:1], g_vec)]
+
+ def eig_scan(carry, x):
+ k, prm = x
+ k = jnp.expand_dims(k, axis=0)
+ eig = eig_fn(prm, k, g_vec)
+ return None, eig
+
+ carry, ys = jax.lax.scan(eig_scan, None, xs=(kpts[1:], params_fine_tuning))
+ eigen_values += list(ys)
+
+ return eigen_values
+
+ num_devices = jax.device_count()
+ k_path = jnp.reshape(k_path, (num_devices, -1, 3))
+ params_pw_band = jax.tree.map(
+ lambda x: jnp.stack([x] * num_devices, axis=0), params_pw_band
+ )
+ opt_state = jax.tree.map(
+ lambda x: jnp.stack([x] * num_devices, axis=0), opt_state
+ )
+ time_start = time.time()
+ eigen_values = optimize_eigenvalues(k_path, params_pw_band, opt_state)
+ time_end = time.time()
+ logging.info(
+ f"Band structure calculation: {time_end - time_start:.2f} seconds"
+ )
+
+ eigen_values = jnp.stack(eigen_values)
+ num_spin = eigen_values.shape[2]
+ eigen_values = jnp.reshape(
+ eigen_values, (config.num_kpoints, num_spin, num_bands), order="F"
+ )
+ eigen_values = jnp.transpose(eigen_values, (1, 0, 2))
+
+ logging.info("===> Eigen decomposition done.")
+ save_file = ''.join(crystal.symbol) + "_band_structure.npy"
+ logging.info(f"Results saved in {save_file}")
+ jnp.save(save_file, eigen_values)
diff --git a/jrystal/calc/calc_band_structure_normcons.py b/jrystal/calc/calc_band_structure_normcons.py
index 8e0d545..b4464be 100644
--- a/jrystal/calc/calc_band_structure_normcons.py
+++ b/jrystal/calc/calc_band_structure_normcons.py
@@ -12,33 +12,40 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Band Structure Calculator. """
+import time
+from dataclasses import dataclass
+from functools import partial
+from math import ceil
+from typing import Optional
+
import jax
import jax.numpy as jnp
+import numpy as np
import optax
-from math import ceil
-
-import time
-from typing import Optional
-from dataclasses import dataclass
from absl import logging
from tqdm import tqdm
-from .calc_ground_state_energy_normcons import calc as energy_calc
-from .calc_ground_state_energy_normcons import GroundStateEnergyOutput
-from .opt_utils import set_env_params, create_crystal, create_freq_mask
-from .opt_utils import create_grids, create_optimizer, create_pseudopotential
-
-from ..config import JrystalConfigDict
-from .._src import pw, occupation
+from .._src import pw
from .._src.band import get_k_path
from .._src.crystal import Crystal
-from ..pseudopotential import local, normcons
+from ..config import JrystalConfigDict
+from ..pseudopotential import local, nloc
+from .calc_ground_state_energy_normcons import GroundStateEnergyOutput
+from .calc_ground_state_energy_normcons import calc as energy_calc
+from .opt_utils import (
+ create_crystal,
+ create_freq_mask,
+ create_grids,
+ create_optimizer,
+ create_pseudopotential,
+ set_env_params,
+)
@dataclass
class BandStructureOutput:
- """Output of the band structure calculation.
-
+ """Output of the band structure calculation.
+
Args:
config (JrystalConfigDict): The configuration for the calculation.
crystal (Crystal): The crystal object.
@@ -71,9 +78,12 @@ def calc(
set_env_params(config)
key = jax.random.PRNGKey(config.seed)
crystal = create_crystal(config)
+ pseudopot = create_pseudopotential(config)
+ valence_charges = np.sum(pseudopot.valence_charges)
g_vec, r_vec, k_vec = create_grids(config)
freq_mask = create_freq_mask(config)
xc = config.xc
+ logging.info(f"XC functional: {config.xc}")
# generate K-path.
logging.info("===> Generating K-path...")
@@ -85,8 +95,14 @@ def calc(
)
logging.info(f"{k_path.shape[0]} k-points generated.")
- # initialize pseudopotential
- pseudopot = create_pseudopotential(config)
+ # optimitimize ground state energy if not provided.
+ if ground_state_energy_output is None:
+ logging.info("===> Starting total energy minimization...")
+ start = time.time()
+ ground_state_density_grid = energy_calc(config)
+
+ jax.clear_caches()
+ logging.info("Initializing pseudopotential (local)...")
potential_loc = local._potential_local_reciprocal(
crystal.positions,
g_vec,
@@ -95,65 +111,58 @@ def calc(
pseudopot.local_potential_charge,
crystal.vol
)
- potential_nl = normcons._potential_nonlocal_square_root(
- crystal.positions,
- g_vec,
- k_vec,
+ logging.info("Initializing pseudopotential (Spherical Bessel Transform)...")
+ start = time.time()
+ beta_gk = nloc.beta_sbt_grid_multi_atoms(
pseudopot.r_grid,
pseudopot.nonlocal_beta_grid,
pseudopot.nonlocal_angular_momentum,
+ g_vec,
+ k_path,
+ ) # shape: [kpt beta x y z]
+ end = time.time()
+ logging.info(
+ f"Spherical Bessel Transform done. Times: {end - start:.2f} seconds"
)
- # optimitimize ground state energy if not provided.
- if ground_state_energy_output is None:
- logging.info("===> Starting total energy minimization...")
- start = time.time()
- ground_state_energy_output = energy_calc(config)
- logging.info(
- f" Total energy minimization done. "
- f"Time: {time.time()-start:.3f} "
- )
-
- params_pw_ground_state = ground_state_energy_output.params_pw
- params_occ_ground_state = ground_state_energy_output.params_occ
-
- # Calculate ground state density at grid points.
- def get_occupation(params):
- return occupation.idempotent(
- params, crystal.num_electron, k_vec.shape[0], crystal.spin
+ def get_potential_nl(kpt, beta_gk):
+ return nloc._potential_nonlocal_square_root(
+ crystal.positions,
+ g_vec,
+ kpt,
+ pseudopot.r_grid,
+ pseudopot.nonlocal_beta_grid,
+ pseudopot.nonlocal_angular_momentum,
+ pseudopot.nonlocal_d_matrix,
+ beta_gk
)
- coeff_ground_state = pw.coeff(params_pw_ground_state, freq_mask)
- occ_ground_state = get_occupation(params_occ_ground_state)
- ground_state_density_grid = pw.density_grid(
- coeff_ground_state, crystal.vol, occ_ground_state
- )
-
- # Define the objective function for band structure calculation.
- def hamiltonian_trace(params_pw_band, kpts):
+ def hamiltonian_trace(params_pw_band, kpts, g_vector_grid, potential_nl):
coeff_band = pw.coeff(params_pw_band, freq_mask)
-
- return normcons._hamiltonian_trace(
+ return nloc._hamiltonian_trace(
coeff_band,
ground_state_density_grid,
potential_loc,
potential_nl,
- g_vec,
+ g_vector_grid,
kpts,
- pseudopot.nonlocal_d_matrix,
- crystal.vol
+ crystal.vol,
+ xc=xc,
+ kohn_sham=True
)
# Initialize parameters and optimizer.
optimizer = create_optimizer(config)
- num_bands = ceil(crystal.num_electron / 2) + config.band_structure_empty_bands
+ num_bands = ceil(valence_charges / 2) + config.band_structure_empty_bands
params_pw_band = pw.param_init(key, num_bands, 1, freq_mask)
opt_state = optimizer.init(params_pw_band)
# define update function
@jax.jit
- def update(params, opt_state, kpts):
- hamil_trace, grad = jax.value_and_grad(hamiltonian_trace)(params, kpts)
+ def update(params, opt_state, kpts, g_vector_grid, potential_nl):
+ hamil_trace, grad = jax.value_and_grad(hamiltonian_trace)(
+ params, kpts, g_vector_grid, potential_nl
+ )
updates, opt_state = optimizer.update(grad, opt_state)
params = optax.apply_updates(params, updates)
@@ -162,86 +171,115 @@ def update(params, opt_state, kpts):
# the main loop for band structure calculation.
logging.info("===> Starting band structure calculation...")
+ logging.info(f"Number of bands: {num_bands}")
logging.info("Optimizing the first K point...")
- params_kpoint_list = []
- eigen_values = []
+ @partial(jax.pmap, in_axes=(0, 0, 0, 0), devices=jax.devices())
+ def optimize_eigenvalues(kpts, beta_gk, params_pw_band, opt_state):
- if config.verbose:
- iters = tqdm(range(config.band_structure_epoch))
- else:
- iters = tqdm(range(config.band_structure_epoch), disable=True)
+ logging.info("===> Optimizing the first K point...")
+ eigen_values = []
- start = time.time()
- for i in iters:
- params_pw_band, opt_state, hamil_trace = update(
- params_pw_band, opt_state, k_path[0:1]
- )
- iters.set_description(f"Hamiltonian trace: {hamil_trace:.4E}")
+ def update_scan(carry, xs):
+ params_pw_band, opt_state, potential_nl_k, kpts = carry
+ params_pw_band, opt_state, hamil_trace = update(
+ params_pw_band, opt_state, kpts, g_vec, potential_nl_k
+ )
+ return (params_pw_band, opt_state, potential_nl_k, kpts), None
- # TODO: introduce convergence tracker.
- converged = True
+ potential_nl_k = get_potential_nl(kpts[0:1], beta_gk[0:1])
+ carry, _ = jax.lax.scan(
+ update_scan, (params_pw_band, opt_state, potential_nl_k, kpts[0:1]),
+ length=config.band_structure_epoch, unroll = 1
+ )
+ params_first_kpt, opt_state, _, _ = carry
- params_kpoint_list.append(params_pw_band)
- logging.info(f" Converged: {converged}.")
- logging.info(f" Total epochs run: {i+1}.")
- logging.info(f" Training Time: {(time.time() - start):.3f}s.")
- logging.info("===> Starting fine-tuning the rest k points...")
- logging.info(f" fine tuning steps: {config.k_path_fine_tuning_epoch}")
+ logging.info("===> Fine-tuning the rest K points...")
- if config.verbose:
- iters = tqdm(range(1, k_path.shape[0]))
- else:
- iters = tqdm(range(1, k_path.shape[0]), disable=True)
+ def finetuning(carry, x):
+ kpts, beta_gk = x
+ kpts = jnp.expand_dims(kpts, axis=0)
+ beta_gk = jnp.expand_dims(beta_gk, axis=0)
+ params_pw_band, opt_state = carry
+ potential_nl_k = get_potential_nl(kpts, beta_gk)
- for i in iters:
- for _ in range(config.k_path_fine_tuning_epoch):
- params_pw_band, opt_state, hamil_trace = update(
- params_pw_band, opt_state, k_path[i:(i+1)]
+ carry, _ = jax.lax.scan(
+ update_scan, (params_pw_band, opt_state, potential_nl_k, kpts),
+ length=config.k_path_fine_tuning_epoch, unroll = 1
)
- iters.set_description(f" Loss(the {i+1}th k point): {hamil_trace:.4E}")
- params_kpoint_list.append(params_pw_band)
+ params_pw_band, opt_state, _, _ = carry
- logging.info("===> Band structure calculation done.")
- ########################################################
- # One-time eigen decomposition
- logging.info("===> Diagonalizing the Hamiltonian matrix...")
+ return (params_pw_band, opt_state), params_pw_band
- @jax.jit
- def eig_fn(param, kpts):
- coeff_i = pw.coeff(param, freq_mask)
- hamil_matrix = normcons._hamiltonian_matrix(
- coeff_i,
- ground_state_density_grid,
- potential_loc,
- potential_nl,
- g_vec,
- kpts,
- pseudopot.nonlocal_d_matrix,
- crystal.vol
+ carry, params_fine_tuning = jax.lax.scan(
+ finetuning, (params_first_kpt, opt_state),
+ xs = (kpts[1:], beta_gk[1:])
)
- return jnp.linalg.eigvalsh(hamil_matrix[0])
+ logging.info("===> Eigen decomposition...")
+
+ def eig_fn(param, kpts, potential_nl, g_vector_grid):
+ coeff_i = pw.coeff(param, freq_mask)
+ hamil_matrix = nloc._hamiltonian_matrix(
+ coeff_i,
+ ground_state_density_grid,
+ potential_loc,
+ potential_nl,
+ g_vector_grid,
+ kpts,
+ crystal.vol,
+ xc,
+ kohn_sham=True
+ )
+ return jnp.linalg.eigvalsh(hamil_matrix[0])
+
+ eigen_values = [
+ eig_fn(
+ params_first_kpt,
+ kpts[0:1],
+ get_potential_nl(kpts[0:1], beta_gk[0:1]),
+ g_vec
+ )
+ ]
+
+ def eig_scan(carry, x):
+ k, b, prm = x
+ k = jnp.expand_dims(k, axis=0)
+ b = jnp.expand_dims(b, axis=0)
+ potential_nl_k = get_potential_nl(k, b)
+ eig = eig_fn(prm, k, potential_nl_k, g_vec)
+ return None, eig
+
+ carry, ys = jax.lax.scan(
+ eig_scan, None, xs=(kpts[1:], beta_gk[1:], params_fine_tuning)
+ )
+ eigen_values += list(ys)
- iters = tqdm(range(len(params_kpoint_list)))
- for i in iters:
- prm = params_kpoint_list[i]
- k = k_path[i:(i + 1), :]
- iters.set_description(f"Diagonolizing the {i+1}th k points")
- eig = eig_fn(prm, k)
- eigen_values.append(eig)
+ return eigen_values
+
+ num_devices = jax.device_count()
+ k_path = jnp.reshape(k_path, (num_devices, -1, 3))
+ beta_gk = jnp.reshape(beta_gk, (num_devices, -1, *beta_gk.shape[1:]))
+ params_pw_band = jax.tree.map(
+ lambda x: jnp.stack([x] * num_devices, axis=0), params_pw_band
+ )
+ opt_state = jax.tree.map(
+ lambda x: jnp.stack([x] * num_devices, axis=0), opt_state
+ )
+ time_start = time.time()
+ eigen_values = optimize_eigenvalues(
+ k_path, beta_gk, params_pw_band, opt_state
+ )
+ time_end = time.time()
+ logging.info(
+ f"Band structure calculation: {time_end - time_start:.2f} seconds"
+ )
+ eigen_values = jnp.stack(eigen_values)
+ eigen_values = jnp.reshape(
+ eigen_values, (-1, eigen_values.shape[-1]), order="F"
+ )
- eigen_values = jnp.vstack(eigen_values)
logging.info("===> Eigen decomposition done.")
save_file = ''.join(crystal.symbol) + "_band_structure.npy"
logging.info(f"Results saved in {save_file}")
jnp.save(save_file, eigen_values)
-
- return BandStructureOutput(
- config=config,
- crystal=crystal,
- params_pw=params_pw_band,
- ground_state_energy_output=ground_state_energy_output,
- k_path=k_path,
- band_structure=eigen_values,
- )
diff --git a/jrystal/calc/calc_ground_state_energy.py b/jrystal/calc/calc_ground_state_energy.py
deleted file mode 100644
index b3690ab..0000000
--- a/jrystal/calc/calc_ground_state_energy.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright 2025 Garena Online Private Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import time
-from dataclasses import dataclass
-from math import ceil
-from typing import List, Union
-
-import jax
-import optax
-from absl import logging
-from tqdm import tqdm
-
-from .._src.crystal import Crystal
-from .._src import energy, entropy, occupation, pw
-from ..config import JrystalConfigDict
-from .opt_utils import (
- create_crystal,
- create_freq_mask,
- create_grids,
- create_optimizer,
- get_ewald_coulomb_repulsion,
- set_env_params
-)
-
-
-@dataclass
-class GroundStateEnergyOutput:
- """Output of the ground state energy calculation.
-
- Args:
- config (JrystalConfigDict): Configuration for the calculation.
- crystal (Crystal): The crystal object.
- params_pw (dict): Parameters for the plane wave basis.
- params_occ (dict): Parameters for the occupation.
- total_energy (Union[float, jax.Array]): Total energy of the crystal.
- total_energy_history (List[float]): The optimization history of the total energy.
- """
- config: JrystalConfigDict
- crystal: Crystal
- params_pw: dict
- params_occ: dict
- total_energy: Union[float, jax.Array]
- total_energy_history: List[float]
-
-
-def calc(config: JrystalConfigDict) -> GroundStateEnergyOutput:
- """Calculate the ground state energy of a crystal.
-
- Args:
- config (JrystalConfigDict): Configuration for the calculation.
-
- Returns:
- GroundStateEnergyOutput: The ground state energy of the crystal.
- """
-
- # check if using correct calculator.
- if config.use_pseudopotential:
- raise RuntimeError(
- "This calculator does not support pseudopotential. It only supports all electron calculations. For norm-conserving pseudopotential calculations, please use the `calc_ground_state_energy_normcons.calc` function."
- )
-
- # Initialize and Prepare variables.
- set_env_params(config)
- key = jax.random.PRNGKey(config.seed)
- temp = config.smearing
-
- crystal = create_crystal(config)
- logging.info(f"Crystal: {crystal.symbol}")
- EPS = config.eps
-
- g_vec, r_vec, k_vec = create_grids(config)
- num_kpts = k_vec.shape[0]
- num_bands = ceil(crystal.num_electron / 2) + config.empty_bands
- freq_mask = create_freq_mask(config)
- ew = get_ewald_coulomb_repulsion(config)
-
- # Define functions for energy calculation.
- # assume fermi-dirac occupation.
- logging.info(f"Potentially-occupied bands: {num_bands}")
-
- def get_occupation(params):
- return occupation.idempotent(
- params, crystal.num_electron, num_kpts, crystal.spin
- )
-
- def total_energy(params_pw, params_occ):
- coeff = pw.coeff(params_pw, freq_mask)
- occ = get_occupation(params_occ)
- return energy.total_energy(
- coeff,
- crystal.positions,
- crystal.charges,
- g_vec,
- k_vec,
- crystal.vol,
- occ,
- kohn_sham=False,
- )
-
- def get_entropy(params_occ):
- occ = get_occupation(params_occ)
- return entropy.fermi_dirac(occ, eps=EPS)
-
- def free_energy(params_pw, params_occ, temp):
- total = total_energy(params_pw, params_occ)
- etro = get_entropy(params_occ)
- free = total + temp * etro
- return free, (total, etro)
-
- # Initialize parameters and optimizer.
- optimizer = create_optimizer(config)
- params_pw = pw.param_init(key, num_bands, num_kpts, freq_mask)
- params_occ = occupation.idempotent_param_init(key, num_bands, num_kpts)
- params = {"pw": params_pw, "occ": params_occ}
- opt_state = optimizer.init(params)
-
- # Define update function.
- @jax.jit
- def update(params, opt_state, temp):
- loss = lambda x: free_energy(x["pw"], x["occ"], temp)
- (loss_val, es), grad = jax.value_and_grad(loss, has_aux=True)(params)
- updates, opt_state = optimizer.update(grad, opt_state)
- params = optax.apply_updates(params, updates)
- return params, opt_state, loss_val, es
-
- # Define scheduler for temperature annealing.
- if config.smearing > 0.:
- temperature_scheduler = optax.exponential_decay(
- init_value=100.,
- transition_steps=config.epoch // 2,
- decay_rate=config.smearing / 100,
- end_value=config.smearing
- )
- else:
-
- def temperature_scheduler(i):
- return 0.
-
- logging.info(f"smearing: {config.smearing}")
-
- # The main loop for optimization.
- if config.verbose:
- iters = tqdm(range(config.epoch))
- else:
- iters = tqdm(range(config.epoch), disable=True)
-
- train_time = 0
- for i in iters:
- temp = temperature_scheduler(i)
- start = time.time()
- params, opt_state, loss_val, es = update(params, opt_state, temp)
- etot, entro = es
- etot = jax.block_until_ready(etot)
- train_time += time.time() - start
-
- iters.set_description(
- f"Loss: {loss_val:.4f}|Energy: {etot+ew:.4f}|"
- f"Entropy: {entro:.4f}|T: {temp:.2E}"
- )
-
- #####################################
- # END OF OPTIMIZATION #
- #####################################
- coeff = pw.coeff(params["pw"], freq_mask)
- occ = get_occupation(params["occ"])
- density = pw.density_grid(coeff, crystal.vol, occ)
- density_reciprocal = pw.density_grid_reciprocal(coeff, crystal.vol, occ)
- kinetic = energy.kinetic(g_vec, k_vec, coeff, occ)
- hartree = energy.hartree(density_reciprocal, g_vec, crystal.vol)
- external = energy.external(
- density_reciprocal, crystal.positions, crystal.charges, g_vec, crystal.vol
- )
- lda = energy.xc_lda(density, crystal.vol)
-
- logging.info(f"Hartree Energy: {hartree:.4f} Ha")
- logging.info(f"External Energy: {external:.4f} Ha")
- logging.info(f"LDA Energy: {lda:.4f} Ha")
- logging.info(f"Kinetic Energy: {kinetic:.4f} Ha")
- logging.info(f"Nuclear repulsion Energy: {ew:.4f} Ha")
- logging.info(f"Total Energy: {etot+ew:.4f} Ha")
-
- return GroundStateEnergyOutput(
- config, crystal, params["pw"], params["occ"], etot + ew, []
- )
diff --git a/jrystal/calc/calc_ground_state_energy_all_electrons.py b/jrystal/calc/calc_ground_state_energy_all_electrons.py
new file mode 100644
index 0000000..ae6b2c3
--- /dev/null
+++ b/jrystal/calc/calc_ground_state_energy_all_electrons.py
@@ -0,0 +1,241 @@
+# Copyright 2025 Garena Online Private Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+from dataclasses import dataclass
+from math import ceil
+from typing import List, Union
+
+import numpy as np
+import jax
+import optax
+from absl import logging
+from jax.sharding import Mesh, NamedSharding
+from jax.sharding import PartitionSpec as P
+from tqdm import tqdm
+
+from .._src import energy, entropy, occupation, pw
+from .._src.crystal import Crystal
+from .._src.grid import proper_grid_size
+from ..config import JrystalConfigDict
+from .convergence import create_convergence_checker
+from .opt_utils import (
+ create_crystal,
+ create_freq_mask,
+ create_grids,
+ create_optimizer,
+ set_env_params,
+ get_ewald_coulomb_repulsion
+)
+
+
+@dataclass
+class GroundStateEnergyOutput:
+ """Output of the ground state energy calculation.
+
+ Args:
+ config (JrystalConfigDict): The configuration for the calculation.
+ crystal (Crystal): The crystal object.
+ params_pw (dict): Parameters for the plane wave basis.
+ params_occ (dict): Parameters for the occupation.
+ total_energy (Union[float, jax.Array]): The total energy of the crystal.
+ total_energy_history (List[float]): The optimization history of the total energy.
+ """
+ config: JrystalConfigDict
+ crystal: Crystal
+ params_pw: dict
+ params_occ: dict
+ total_energy: Union[float, jax.Array]
+ total_energy_history: List[float]
+
+
+def calc(config: JrystalConfigDict) -> GroundStateEnergyOutput:
+ """Calculate the ground state energy of a crystal for all-electron calculations.
+
+ Args:
+ config (JrystalConfigDict): The configuration for the calculation.
+
+ Returns:
+ GroundStateEnergyOutput: The ground state energy output of the crystal.
+ """
+ # Initialize and Prepare variables.
+ set_env_params(config)
+ key = jax.random.PRNGKey(config.seed)
+ temp = config.smearing
+
+ crystal = create_crystal(config)
+ num_electrons = crystal.num_electron
+ logging.info(f"Crystal: {crystal.symbol}")
+ EPS = config.eps
+
+ mesh = Mesh(np.array(jax.devices()).reshape([1, -1]), ('s', 'k'))
+ sharding = NamedSharding(mesh, P('s', 'k')) # shard by the kpt dimension.
+ num_devices = len(jax.devices())
+ logging.info(f"Number of devices: {num_devices}")
+
+ g_vec, r_vec, k_vec = create_grids(config)
+ num_kpts = k_vec.shape[0]
+ logging.info(f"Number of G-vectors: {proper_grid_size(config.grid_sizes)}")
+ logging.info(f"Number of k-vectors: {proper_grid_size(config.k_grid_sizes)}")
+ num_bands = ceil(num_electrons / 2) + config.empty_bands
+ logging.info(f"num_bands: {num_bands}")
+ logging.info(f"XC functional: {config.xc}")
+ logging.info(f"Occupation method: {config.occupation}")
+ freq_mask = create_freq_mask(config)
+ ew = get_ewald_coulomb_repulsion(config)
+
+ convergence_checker = create_convergence_checker(config)
+ converged = False
+ k_vec = jax.device_put(k_vec, NamedSharding(mesh, P('k')))
+
+ # Define functions for energy calculation.
+ def get_occupation(params):
+ return occupation.occupation(
+ params,
+ num_kpts,
+ num_electrons,
+ spin=crystal.spin,
+ method=config.occupation,
+ spin_restricted=config.spin_restricted
+ )
+
+ def total_energy(params_pw, params_occ, g_vec):
+ coeff = pw.coeff(params_pw, freq_mask, sharding=sharding)
+ occ = get_occupation(params_occ)
+ density = pw.density_grid(coeff, crystal.vol, occ)
+ density_reciprocal = pw.density_grid_reciprocal(coeff, crystal.vol, occ)
+ kinetic = energy.kinetic(g_vec, k_vec, coeff, occ)
+ hartree = energy.hartree(density_reciprocal, g_vec, crystal.vol)
+ external = energy.external(
+ density_reciprocal,
+ crystal.positions,
+ crystal.charges,
+ g_vec,
+ crystal.vol
+ )
+
+ xc = energy.xc_energy(
+ density, g_vec, crystal.vol, config.xc, kohn_sham=False
+ )
+ return kinetic + hartree + external + xc
+
+ def get_entropy(params_occ):
+ occ = get_occupation(params_occ)
+ return entropy.fermi_dirac(occ, eps=EPS)
+
+ def free_energy(params_pw, params_occ, temp, g_vec):
+ total = total_energy(params_pw, params_occ, g_vec)
+ etro = get_entropy(params_occ)
+ free = total - temp * etro
+ return free, (total, etro)
+
+ # Initialize parameters and optimizer.
+ optimizer = create_optimizer(config)
+ params_pw = pw.param_init(
+ key,
+ num_bands,
+ num_kpts,
+ freq_mask,
+ spin_restricted=config.spin_restricted,
+ sharding=sharding
+ )
+ params_occ = occupation.param_init(
+ key,
+ num_bands,
+ num_electrons,
+ num_kpts,
+ crystal.spin,
+ config.occupation,
+ spin_restricted=config.spin_restricted
+ )
+ # params_occ = jax.device_put(params_occ, sharding)
+ params = {"pw": params_pw, "occ": params_occ}
+ opt_state = optimizer.init(params)
+
+ # Define update function.
+ with mesh:
+
+ @jax.jit
+ def update(params, opt_state, temp, g_vec):
+ loss = lambda x: free_energy(x["pw"], x["occ"], temp, g_vec)
+ (loss_val, es), grad = jax.value_and_grad(loss, has_aux=True)(params)
+ updates, opt_state = optimizer.update(grad, opt_state)
+ params = optax.apply_updates(params, updates)
+ return params, opt_state, loss_val, es
+
+ # Define scheduler for temperature annealing.
+ if config.smearing > 0.:
+ temperature_scheduler = optax.exponential_decay(
+ init_value=100.,
+ transition_steps=config.epoch // 2,
+ decay_rate=config.smearing / 100,
+ end_value=config.smearing
+ )
+ else:
+
+ def temperature_scheduler(i):
+ return 0.
+
+ logging.info(f"smearing: {config.smearing}")
+
+ # The main loop for optimization.
+ if config.verbose:
+ iters = tqdm(range(config.epoch))
+ else:
+ iters = tqdm(range(config.epoch), disable=True)
+
+ train_time = 0
+ for i in iters:
+ temp = temperature_scheduler(i)
+ start = time.time()
+ params, opt_state, loss_val, es = update(params, opt_state, temp, g_vec)
+ etot, entro = es
+ etot = jax.block_until_ready(etot)
+ train_time += time.time() - start
+ converged = convergence_checker.check(etot)
+ if converged:
+ logging.info("Converged.")
+ break
+
+ iters.set_description(
+ f"Loss: {loss_val:.4f}|Energy: {etot+ew:.4f}|"
+ f"Entropy: {entro:.4f}|T: {temp:.2E}"
+ )
+
+ if not converged:
+ logging.warning("Did not converge.")
+
+ #####################################
+ # END OF OPTIMIZATION #
+ #####################################
+ coeff = pw.coeff(params["pw"], freq_mask)
+ occ = get_occupation(params["occ"])
+ density = pw.density_grid(coeff, crystal.vol, occ)
+ density_reciprocal = pw.density_grid_reciprocal(coeff, crystal.vol, occ)
+ kinetic = energy.kinetic(g_vec, k_vec, coeff, occ)
+ hartree = energy.hartree(density_reciprocal, g_vec, crystal.vol)
+ external = energy.external(
+ density_reciprocal, crystal.positions, crystal.charges, g_vec, crystal.vol
+ )
+
+ xc = energy.xc_energy(density, g_vec, crystal.vol, config.xc, kohn_sham=False)
+
+ logging.info(f"Hartree Energy: {hartree:.4f} Ha")
+ logging.info(f"External Energy: {external:.4f} Ha")
+ logging.info(f"XC Energy: {xc:.4f} Ha")
+ logging.info(f"Kinetic Energy: {kinetic:.4f} Ha")
+ logging.info(f"Nuclear repulsion Energy: {ew:.4f} Ha")
+ logging.info(f"Total Energy: {etot+ew:.4f} Ha")
+
+ return density
diff --git a/jrystal/calc/calc_ground_state_energy_normcons.py b/jrystal/calc/calc_ground_state_energy_normcons.py
index f7de546..59ac1f6 100644
--- a/jrystal/calc/calc_ground_state_energy_normcons.py
+++ b/jrystal/calc/calc_ground_state_energy_normcons.py
@@ -17,30 +17,35 @@
from math import ceil
from typing import List, Union
+import numpy as np
import jax
import optax
from absl import logging
+from jax.sharding import Mesh, NamedSharding
+from jax.sharding import PartitionSpec as P
from tqdm import tqdm
-from .._src.crystal import Crystal
from .._src import energy, entropy, occupation, pw
+from .._src.crystal import Crystal
+from .._src.grid import proper_grid_size
from ..config import JrystalConfigDict
-from ..pseudopotential import local, normcons
+from ..pseudopotential import local, nloc
+from .convergence import create_convergence_checker
from .opt_utils import (
create_crystal,
create_freq_mask,
create_grids,
create_optimizer,
create_pseudopotential,
- get_ewald_coulomb_repulsion,
- set_env_params
+ set_env_params,
+ get_ewald_coulomb_repulsion
)
@dataclass
class GroundStateEnergyOutput:
- """Output of the ground state energy calculation.
-
+ """Output of the ground state energy calculation.
+
Args:
config (JrystalConfigDict): The configuration for the calculation.
crystal (Crystal): The crystal object.
@@ -72,17 +77,31 @@ def calc(config: JrystalConfigDict) -> GroundStateEnergyOutput:
temp = config.smearing
crystal = create_crystal(config)
+ pseudopot = create_pseudopotential(config)
+ valence_charges = np.sum(pseudopot.valence_charges)
logging.info(f"Crystal: {crystal.symbol}")
EPS = config.eps
+ mesh = Mesh(np.array(jax.devices()).reshape([1, -1]), ('s', 'k'))
+ sharding = NamedSharding(mesh, P('s', 'k')) # shard by the kpt dimension.
+ num_devices = len(jax.devices())
+ logging.info(f"Number of devices: {num_devices}")
+
g_vec, r_vec, k_vec = create_grids(config)
num_kpts = k_vec.shape[0]
- num_bands = ceil(crystal.num_electron / 2) + config.empty_bands
+ logging.info(f"Number of G-vectors: {proper_grid_size(config.grid_sizes)}")
+ logging.info(f"Number of k-vectors: {proper_grid_size(config.k_grid_sizes)}")
+ num_bands = ceil(valence_charges / 2) + config.empty_bands
+ logging.info(f"num_bands: {num_bands}")
+ logging.info(f"XC functional: {config.xc}")
freq_mask = create_freq_mask(config)
ew = get_ewald_coulomb_repulsion(config)
+ valence_charges = np.sum(pseudopot.valence_charges)
+ convergence_checker = create_convergence_checker(config)
+ converged = False
# initialize pseudopotential
- pseudopot = create_pseudopotential(config)
+ logging.info("Initializing pseudopotential (local)...")
potential_loc = local._potential_local_reciprocal(
crystal.positions,
g_vec,
@@ -91,24 +110,58 @@ def calc(config: JrystalConfigDict) -> GroundStateEnergyOutput:
pseudopot.local_potential_charge,
crystal.vol
)
- potential_nl = normcons._potential_nonlocal_square_root(
+
+ k_vec = jax.device_put(k_vec, NamedSharding(mesh, P('k')))
+ logging.info("Initializing pseudopotential (Spherical Bessel Transform)...")
+ start = time.time()
+ beta_gk = nloc.beta_sbt_grid_multi_atoms(
+ pseudopot.r_grid,
+ pseudopot.nonlocal_beta_grid,
+ pseudopot.nonlocal_angular_momentum,
+ g_vec,
+ k_vec,
+ )
+ beta_gk = jax.device_put(beta_gk, NamedSharding(mesh, P('k')))
+ end = time.time()
+ logging.info(
+ f"Spherical Bessel Transform done. Times: {end - start:.2f} seconds"
+ )
+ logging.info("Initializing pseudopotential (nonlocal)...")
+ start = time.time()
+ potential_nl = nloc._potential_nonlocal_square_root(
crystal.positions,
g_vec,
k_vec,
pseudopot.r_grid,
pseudopot.nonlocal_beta_grid,
pseudopot.nonlocal_angular_momentum,
+ pseudopot.nonlocal_d_matrix,
+ beta_gk
+ ) # shape "kpt beta phi x y z"
+ del beta_gk
+ end = time.time()
+ logging.info(f"Nonlocal potential done. Times: {end - start:.2f} seconds")
+ logging.info("Deploying pseudopotential (nonlocal)...")
+ start = time.time()
+ potential_nl = jax.device_put(potential_nl, NamedSharding(mesh, P('k')))
+ end = time.time()
+ logging.info(
+ f"Deploying pseudopotential (nonlocal) done. Times: {end - start:.2f} seconds"
)
# Define functions for energy calculation.
- # assume fermi-dirac occupation.
def get_occupation(params):
- return occupation.idempotent(
- params, crystal.num_electron, num_kpts, crystal.spin
+ return occupation.occupation(
+ params,
+ num_kpts,
+ num_electrons=np.sum(pseudopot.valence_charges),
+ spin=crystal.spin,
+ method=config.occupation,
+ spin_restricted=config.spin_restricted
)
- def total_energy(params_pw, params_occ, g_vec):
- coeff = pw.coeff(params_pw, freq_mask)
+ def total_energy(params_pw, params_occ, g_vec, potential_loc, potential_nl):
+ coeff = pw.coeff(params_pw, freq_mask, sharding=sharding)
occ = get_occupation(params_occ)
density = pw.density_grid(coeff, crystal.vol, occ)
density_reciprocal = pw.density_grid_reciprocal(coeff, crystal.vol, occ)
@@ -117,77 +170,102 @@ def total_energy(params_pw, params_occ, g_vec):
external_local = local._energy_local(
density_reciprocal, v_local_reciprocal=potential_loc, vol=crystal.vol
)
- external_nonlocal = normcons._energy_nonlocal(
- coeff,
- potential_nl,
- pseudopot.nonlocal_d_matrix,
- vol=crystal.vol,
- occupation=occ
+ external_nonlocal = nloc._energy_nonlocal(
+ coeff, potential_nl, vol=crystal.vol, occupation=occ
)
- lda = energy.xc_lda(density, crystal.vol)
- return kinetic + hartree + external_local + external_nonlocal + lda
+ xc = energy.xc_energy(
+ density, g_vec, crystal.vol, config.xc, kohn_sham=False
+ )
+ return kinetic + hartree + external_local + external_nonlocal + xc
def get_entropy(params_occ):
occ = get_occupation(params_occ)
return entropy.fermi_dirac(occ, eps=EPS)
- def free_energy(params_pw, params_occ, temp, g_vec):
- total = total_energy(params_pw, params_occ, g_vec)
+ def free_energy(
+ params_pw, params_occ, temp, g_vec, potential_loc, potential_nl
+ ):
+ total = total_energy(
+ params_pw, params_occ, g_vec, potential_loc, potential_nl
+ )
etro = get_entropy(params_occ)
- free = total + temp * etro
+ free = total - temp * etro
return free, (total, etro)
# Initialize parameters and optimizer.
optimizer = create_optimizer(config)
- params_pw = pw.param_init(key, num_bands, num_kpts, freq_mask)
- params_occ = occupation.idempotent_param_init(key, num_bands, num_kpts)
+ params_pw = pw.param_init(
+ key,
+ num_bands,
+ num_kpts,
+ freq_mask,
+ spin_restricted=config.spin_restricted,
+ sharding=sharding
+ )
+ params_occ = occupation.param_init(
+ key, num_bands, valence_charges, num_kpts, crystal.spin, config.occupation
+ )
+ params_occ = jax.device_put(params_occ, sharding)
params = {"pw": params_pw, "occ": params_occ}
opt_state = optimizer.init(params)
# Define update function.
- @jax.jit
- def update(params, opt_state, temp, g_vec):
- loss = lambda x: free_energy(x["pw"], x["occ"], temp, g_vec)
- (loss_val, es), grad = jax.value_and_grad(loss, has_aux=True)(params)
- updates, opt_state = optimizer.update(grad, opt_state)
- params = optax.apply_updates(params, updates)
- return params, opt_state, loss_val, es
-
- # Define scheduler for temperature annealing.
- if config.smearing > 0.:
- temperature_scheduler = optax.exponential_decay(
- init_value=100.,
- transition_steps=config.epoch // 2,
- decay_rate=config.smearing / 100,
- end_value=config.smearing
- )
- else:
-
- def temperature_scheduler(i):
- return 0.
-
- logging.info(f"smearing: {config.smearing}")
-
- # The main loop for optimization.
- if config.verbose:
- iters = tqdm(range(config.epoch))
- else:
- iters = tqdm(range(config.epoch), disable=True)
-
- train_time = 0
- for i in iters:
- temp = temperature_scheduler(i)
- start = time.time()
- params, opt_state, loss_val, es = update(params, opt_state, temp, g_vec)
- etot, entro = es
- etot = jax.block_until_ready(etot)
- train_time += time.time() - start
-
- iters.set_description(
- f"Loss: {loss_val:.4f}|Energy: {etot+ew:.4f}|"
- f"Entropy: {entro:.4f}|T: {temp:.2E}"
- )
+ with mesh:
+
+ @jax.jit
+ def update(params, opt_state, temp, g_vec, potential_nl):
+ loss = lambda x: free_energy(
+ x["pw"], x["occ"], temp, g_vec, potential_loc, potential_nl
+ )
+ (loss_val, es), grad = jax.value_and_grad(loss, has_aux=True)(params)
+ updates, opt_state = optimizer.update(grad, opt_state)
+ params = optax.apply_updates(params, updates)
+ return params, opt_state, loss_val, es
+
+ # Define scheduler for temperature annealing.
+ if config.smearing > 0.:
+ temperature_scheduler = optax.exponential_decay(
+ init_value=100.,
+ transition_steps=config.epoch // 2,
+ decay_rate=config.smearing / 100,
+ end_value=config.smearing
+ )
+ else:
+
+ def temperature_scheduler(i):
+ return 0.
+
+ logging.info(f"smearing: {config.smearing}")
+
+ # The main loop for optimization.
+ if config.verbose:
+ iters = tqdm(range(config.epoch))
+ else:
+ iters = tqdm(range(config.epoch), disable=True)
+
+ train_time = 0
+ for i in iters:
+ temp = temperature_scheduler(i)
+ start = time.time()
+ params, opt_state, loss_val, es = update(
+ params, opt_state, temp, g_vec, potential_nl
+ )
+ etot, entro = es
+ etot = jax.block_until_ready(etot)
+ train_time += time.time() - start
+ converged = convergence_checker.check(etot)
+ if converged:
+ logging.info("Converged.")
+ break
+
+ iters.set_description(
+ f"Loss: {loss_val:.4f}|Energy: {etot+ew:.4f}|"
+ f"Entropy: {entro:.4f}|T: {temp:.2E}"
+ )
+
+ if not converged:
+ logging.warning("Did not converge.")
#####################################
# END OF OPTIMIZATION #
@@ -201,24 +279,18 @@ def temperature_scheduler(i):
external_local = local._energy_local(
density_reciprocal, v_local_reciprocal=potential_loc, vol=crystal.vol
)
- external_nonlocal = normcons._energy_nonlocal(
- coeff,
- potential_nl,
- pseudopot.nonlocal_d_matrix,
- vol=crystal.vol,
- occupation=occ
+ external_nonlocal = nloc._energy_nonlocal(
+ coeff, potential_nl, vol=crystal.vol, occupation=occ
)
- lda = energy.xc_lda(density, crystal.vol)
+ xc = energy.xc_energy(density, g_vec, crystal.vol, config.xc, kohn_sham=False)
logging.info(f"Hartree Energy: {hartree:.4f} Ha")
logging.info(f"External (local) Energy: {external_local:.4f} Ha")
logging.info(f"External (nonlocal) Energy: {external_nonlocal:.4f} Ha")
- logging.info(f"LDA Energy: {lda:.4f} Ha")
+ logging.info(f"XC Energy: {xc:.4f} Ha")
logging.info(f"Kinetic Energy: {kinetic:.4f} Ha")
logging.info(f"Nuclear repulsion Energy: {ew:.4f} Ha")
logging.info(f"Total Energy: {etot+ew:.4f} Ha")
- return GroundStateEnergyOutput(
- config, crystal, params["pw"], params["occ"], etot + ew, []
- )
+ return density
diff --git a/jrystal/calc/convergence.py b/jrystal/calc/convergence.py
new file mode 100644
index 0000000..f166b94
--- /dev/null
+++ b/jrystal/calc/convergence.py
@@ -0,0 +1,35 @@
+import numpy as np
+
+from ..config import JrystalConfigDict
+
+
+def create_convergence_checker(config: JrystalConfigDict):
+ return ConvergenceChecker(
+ window_size=config.convergence_window_size,
+ threshold=config.convergence_condition,
+ )
+
+
+class ConvergenceChecker:
+
+ def __init__(self, window_size: int = 20, threshold: float = 1e-5):
+ self.window_size = window_size
+ self.threshold = threshold
+ self.history = []
+
+ def check(self, value: float) -> bool:
+ self.history.append(value)
+
+ if len(self.history) > self.window_size:
+ self.history.pop(0)
+
+ if len(self.history) < self.window_size:
+ return False
+
+ if np.std(self.history) < self.threshold:
+ return True
+
+ return False
+
+ def reset(self):
+ self.history = []
diff --git a/jrystal/calc/opt_utils.py b/jrystal/calc/opt_utils.py
index 56df15c..8282c3e 100644
--- a/jrystal/calc/opt_utils.py
+++ b/jrystal/calc/opt_utils.py
@@ -12,33 +12,50 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utility functions for optimization. """
+import argparse
+import os
+from typing import Callable, Union
+
import jax
import numpy as np
import optax
-from optax._src import alias
-import argparse
from absl import logging
-from typing import Callable
+from optax._src import alias
import jrystal as jr
-from .._src.ewald import ewald_coulomb_repulsion
-from .._src.grid import proper_grid_size, translation_vectors
-from .._src.grid import g_vectors, r_vectors, k_vectors
-from .._src.grid import cubic_mask, spherical_mask
-from .._src.grid import estimate_max_cutoff_energy
+
from .._src.crystal import Crystal
+from .._src.ewald import ewald_coulomb_repulsion
+from .._src.grid import (
+ cubic_mask,
+ estimate_max_cutoff_energy,
+ g_vectors,
+ k_vectors,
+ proper_grid_size,
+ r_vectors,
+ spherical_mask,
+ translation_vectors,
+)
+from ..pseudopotential import (
+ NormConservingPseudopotential, UltrasoftPseudopotential
+)
from .._src.utils import check_spin_number
from ..config import JrystalConfigDict
def set_env_params(config: JrystalConfigDict):
- jax.config.update("jax_debug_nans", True)
+ os.environ["OPENBLAS_NUM_THREADS"] = "4"
+ os.environ["MKL_NUM_THREADS"] = "4"
+ os.environ["OMP_NUM_THREADS"] = "4"
+ jax.config.update("jax_debug_nans", config.jax_debug_nans)
if config.verbose:
logging.set_verbosity(logging.INFO)
logging.info('Versbose mode is on.')
if config.jax_enable_x64:
logging.info("Precision: Double (64 bit).")
+ else:
+ logging.info("Precision: Single (32 bit).")
else:
logging.set_verbosity(logging.WARNING)
logging.warning('Versbose mode is off.')
@@ -66,16 +83,21 @@ def create_freq_mask(config: JrystalConfigDict):
if config.freq_mask_method == "cubic":
mask = np.array(cubic_mask(grid_sizes))
max_cutoff = estimate_max_cutoff_energy(crystal.cell_vectors, mask)
- logging.info(f"maxmum cutoff: {max_cutoff:.2f} Ha")
- logging.info(f"number of g points: {np.sum(mask)}")
+ logging.info(
+ f"Maxmum cutoff: {max_cutoff:.0f} Ha ({max_cutoff*27.2114:.0f} eV)"
+ )
+ logging.info(f"Number of g points: {np.sum(mask)}")
elif config.freq_mask_method == "spherical":
mask = spherical_mask(
crystal.cell_vectors, grid_sizes, config.cutoff_energy
)
- logging.info(f"mask percentage: {np.mean(mask)*100:.2f}%")
- logging.info(f"maxmum cutoff: {config.cutoff_energy}")
- logging.info(f"number of g points: {np.sum(mask)}")
+ logging.info(f"Mask percentage: {np.mean(mask)*100:.2f}%")
+ logging.info(
+ f"Maxmum cutoff: {config.cutoff_energy:.0f} Ha "
+ f"({config.cutoff_energy*27.2114:.0f} eV)"
+ )
+ logging.info(f"Number of g points: {np.sum(mask)}")
else:
raise ValueError("freq_mask_method must be either cubic or spherical.")
@@ -85,19 +107,34 @@ def create_freq_mask(config: JrystalConfigDict):
def create_crystal(config: JrystalConfigDict) -> Crystal:
_pkg_path = jr.get_pkg_path()
- path = _pkg_path + '/geometry/' + config.crystal + '.xyz'
- crystal = Crystal.create_from_file(file_path=path)
+ if config.crystal is not None:
+ path = _pkg_path + '/geometry/' + config.crystal + '.xyz'
+ else:
+ path = config.crystal_file_path_path
+ crystal = Crystal.create_from_file(file_path=path, spin=config.spin)
check_spin_number(crystal.num_electron, crystal.spin)
return crystal
-def create_pseudopotential(config: JrystalConfigDict):
+def create_pseudopotential(
+ config: JrystalConfigDict
+) -> Union[NormConservingPseudopotential, UltrasoftPseudopotential]:
assert config.use_pseudopotential
crystal = create_crystal(config)
_pkg_path = jr.get_pkg_path()
- path = _pkg_path + '/pseudopotential/normconserving/'
+ if config.pseudopotential_file_dir is None:
+ path = _pkg_path + '/pseudopotential/' + config.pseudopotential_type + '/'
+ else:
+ path = config.pseudopotential_file_dir
logging.info(f"Pseudopotential path: {path}")
- pp = jr.pseudopotential.NormConservingPseudopotential.create(crystal, path)
+ if config.pseudopotential_type == "normcons":
+ pp = jr.pseudopotential.NormConservingPseudopotential.create(crystal, path)
+ elif config.pseudopotential_type == "ultrasoft":
+ pp = jr.pseudopotential.UltrasoftPseudopotential.create(crystal, path)
+ else:
+ raise ValueError(
+ f"Pseudopotential type {config.pseudopotential_type} is not supported."
+ )
return pp
@@ -116,6 +153,7 @@ def create_optimizer(config: JrystalConfigDict) -> optax.GradientTransformation:
config_dict = dict(config.optimizer_args)
opt = getattr(alias, config.optimizer, None)
lr = config_dict.pop("learning_rate")
+ logging.info(f"learning rate: {lr}")
if config.scheduler:
raise NotImplementedError("Scheduler is not implemented yet.")
@@ -130,8 +168,10 @@ def create_optimizer(config: JrystalConfigDict) -> optax.GradientTransformation:
def create_occupation(config: JrystalConfigDict) -> Callable:
occupation_method = config.occupation
- if occupation_method == "fermi-dirac":
+ if occupation_method == "idempotent":
return jr.occupation.idempotent
+ elif occupation_method == "simplex-projector":
+ return jr.occupation.simplex_projector
elif occupation_method == "uniform":
return jr.occupation.uniform
elif occupation_method == "gamma":
diff --git a/jrystal/config.py b/jrystal/config.py
index b408a06..f4eba17 100644
--- a/jrystal/config.py
+++ b/jrystal/config.py
@@ -12,14 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-from typing import Dict, Union, List, Tuple, Optional
-from ml_collections import ConfigDict
+from typing import Any, Dict, List, Optional, Tuple, Union
+
import yaml
+from ml_collections import ConfigDict
class JrystalConfigDict(ConfigDict):
crystal: Optional[str]
crystal_file_path_path: Optional[str]
+ spin: int
+ save_dir: Optional[str]
+ xc: str
use_pseudopotential: bool
pseudopotential_file_dir: Optional[str]
freq_mask_method: str
@@ -33,8 +37,9 @@ class JrystalConfigDict(ConfigDict):
ewald_args: Dict[str, float]
epoch: int
optimizer: str
- optimizer_args: Dict[str, float]
+ optimizer_args: Dict[str, Any]
scheduler: Optional[str]
+ convergence_window_size: int
convergence_condition: float
band_structure_empty_bands: Optional[int]
k_path_special_points: Optional[str]
@@ -46,6 +51,7 @@ class JrystalConfigDict(ConfigDict):
seed: int
xla_preallocate: bool
jax_enable_x64: bool
+ jax_debug_nans: bool
verbose: bool
eps: float
@@ -53,6 +59,8 @@ class JrystalConfigDict(ConfigDict):
default_config = {
"crystal": "diamond",
"crystal_file_path_path": None,
+ "save_dir": None,
+ "spin": 0,
"xc": "lda_x",
"use_pseudopotential": False,
"pseudopotential_file_dir": None,
@@ -60,7 +68,7 @@ class JrystalConfigDict(ConfigDict):
"cutoff_energy": 100,
"grid_sizes": 64,
"k_grid_sizes": 3,
- "occupation": "fermi-dirac",
+ "occupation": "uniform",
"smearing": 0.001,
"empty_bands": 8,
"spin_restricted": True,
@@ -70,10 +78,13 @@ class JrystalConfigDict(ConfigDict):
"epoch": 5000,
"optimizer": "adam",
"optimizer_args": {
- "learning_rate": 1e-2
+ "learning_rate": 0.01,
+ "b1": 0.9,
+ "b2": 0.99
},
"scheduler": None,
- "convergence_condition": 1e-8,
+ "convergence_window_size": 20,
+ "convergence_condition": 1e-6,
"band_structure_empty_bands": 8,
"k_path_special_points": None,
"num_kpoints": 60,
@@ -84,6 +95,7 @@ class JrystalConfigDict(ConfigDict):
"seed": 123,
"xla_preallocate": True,
"jax_enable_x64": True,
+ "jax_debug_nans": False,
"verbose": True,
"eps": 1e-8,
}
diff --git a/jrystal/energy.py b/jrystal/energy.py
index 81d7182..460f37f 100644
--- a/jrystal/energy.py
+++ b/jrystal/energy.py
@@ -14,21 +14,21 @@
"""Energy functions."""
from ._src.energy import (
- hartree,
+ band_energy,
external,
+ hartree,
kinetic,
- xc_lda,
+ nuclear_repulsion,
total_energy,
- band_energy,
- nuclear_repulsion
+ xc_energy,
)
__all__ = [
"hartree",
"external",
"kinetic",
- "xc_lda",
"total_energy",
"band_energy",
"nuclear_repulsion",
+ "xc_energy",
]
diff --git a/jrystal/grid.py b/jrystal/grid.py
index b5fcac4..069809d 100644
--- a/jrystal/grid.py
+++ b/jrystal/grid.py
@@ -33,6 +33,7 @@
estimate_max_cutoff_energy,
grid_vector_radius,
g2cell_vectors,
+ r2cell_vectors,
r2g_vector_grid,
g2r_vector_grid,
)
@@ -48,6 +49,7 @@
"estimate_max_cutoff_energy",
"grid_vector_radius",
"g2cell_vectors",
+ "r2cell_vectors",
"r2g_vector_grid",
"g2r_vector_grid",
]
diff --git a/jrystal/occupation.py b/jrystal/occupation.py
index 46fdb76..c6e0b87 100644
--- a/jrystal/occupation.py
+++ b/jrystal/occupation.py
@@ -18,6 +18,8 @@
idempotent,
uniform,
gamma,
+ param_init,
+ occupation,
)
__all__ = [
@@ -25,4 +27,6 @@
"idempotent",
"uniform",
"gamma",
+ "param_init",
+ "occupation",
]
diff --git a/jrystal/potential.py b/jrystal/potential.py
index d9cf67b..0bac71e 100644
--- a/jrystal/potential.py
+++ b/jrystal/potential.py
@@ -14,12 +14,11 @@
"""Potential functions."""
from ._src.potential import (
- hartree_reciprocal,
- hartree,
- external_reciprocal,
- external,
- xc_lda,
effective,
+ external,
+ external_reciprocal,
+ hartree,
+ hartree_reciprocal,
)
__all__ = [
@@ -27,6 +26,5 @@
"hartree",
"external_reciprocal",
"external",
- "xc_lda",
"effective",
]
diff --git a/jrystal/pseudopotential/__init__.py b/jrystal/pseudopotential/__init__.py
index 892a1fa..4240096 100644
--- a/jrystal/pseudopotential/__init__.py
+++ b/jrystal/pseudopotential/__init__.py
@@ -27,6 +27,7 @@
from . import load
from . import utils
from . import normcons
+from . import nloc
from . import interpolate
from . import spherical
from . import beta
@@ -41,5 +42,6 @@
'spherical',
'beta',
'utils',
- 'normcons'
+ 'normcons',
+ 'nloc',
]
diff --git a/jrystal/pseudopotential/beta.py b/jrystal/pseudopotential/beta.py
index ac018a8..b38ce84 100644
--- a/jrystal/pseudopotential/beta.py
+++ b/jrystal/pseudopotential/beta.py
@@ -12,9 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Functions for dealing with beta functions. """
-import jax.numpy as jnp
-from typing import List
-from jaxtyping import Float, Array
+import numpy as np
+from typing import List, Optional
+from jaxtyping import Float, Array, Int
from ..sbt import batched_sbt
from .interpolate import cubic_spline
@@ -22,29 +22,33 @@
def beta_sbt_grid_single_atom(
r_grid: Float[Array, "r"],
nonlocal_beta_grid: Float[Array, "beta r"],
- nonlocal_angular_momentum: List[int],
+ nonlocal_angular_momentum: Int[Array, "beta"],
g_vector_grid: Float[Array, "x y z 3"],
-) -> Float[Array, "beta x y z"]:
+ kpts: Optional[Float[Array, "kpt 3"]] = None
+) -> Float[Array, "kpt beta x y z"]:
"""
- Calculate the spherical bessel transform of the beta functions for a single atom.
+ Calculate the spherical bessel transform of the beta functions for a single
+ atom.
.. math::
- \\beta_l(G) = \\int_0^\infty \\beta(r) j_l(Gr) r^2 dr
+ \beta_l(G) = \int_0^\infty \beta(r) j_l(Gr) r^2 dr
- Return the beta function value of angular momentum values :math:`l` at the reciprocal vectors :math:`G` per atom
+ Return the beta function value of angular momentum values :math:`l` at the
+ reciprocal vectors :math:`G` per atom
Args:
r_grid (Float[Array, "r"]): the r grid corresponding to the beta
- functions.
+ functions.
nonlocal_beta_grid (Float[Array, "beta r"]): beta values.
nonlocal_angular_momentum (List[int]): angular momentum corresponding
- to the beta functions.
+ to the beta functions.
g_vector_grid (Float[Array, "x y z 3"]): reciprocal vectors to
- interpolate.
+ interpolate.
+ kpts (Optional[Float[Array, "kpt 3"]]): k-points. Default is None.
Returns:
- Float[Array, "beta x y z"]: the beta functions in reciprocal space.
+ Float[Array, "kptbeta x y z"]: the beta functions in reciprocal space.
.. warning::
Cubic spline interpolation is not implemented in JAX. This function uses ``NumPy`` and is not differentiable.
@@ -53,47 +57,56 @@ def beta_sbt_grid_single_atom(
assert len(nonlocal_angular_momentum) == nonlocal_beta_grid.shape[0]
assert r_grid.shape[0] == nonlocal_beta_grid.shape[1]
- radius = jnp.sqrt(jnp.sum(g_vector_grid**2, axis=-1))
+ nonlocal_angular_momentum = nonlocal_angular_momentum.tolist()
+ if kpts is not None:
+ gk_vector_grid = np.expand_dims(
+ kpts, axis=(1, 2, 3)
+ ) + np.expand_dims(g_vector_grid, 0) # [nk x y z 3]
+ else:
+ gk_vector_grid = np.expand_dims(g_vector_grid, 0) # [1 x y z 3]
+
+ radius = np.sqrt(np.sum(gk_vector_grid**2, axis=-1))
k, beta_k = batched_sbt(
r_grid, nonlocal_beta_grid, l=nonlocal_angular_momentum,
- kmax=jnp.max(radius)
+ kmax=np.max(radius)
)
beta_sbt = cubic_spline(k, beta_k, radius)
+ beta_sbt = np.swapaxes(beta_sbt, 0, 1)
return beta_sbt
def beta_sbt_grid_multi_atoms(
r_grid: List[Float[Array, "r"]],
nonlocal_beta_grid: List[Float[Array, "beta r"]],
- nonlocal_angular_momentum: List[List[int]],
+ nonlocal_angular_momentum: List[Int[Array, "beta"]],
g_vector_grid: Float[Array, "x y z 3"],
-) -> List[Float[Array, "beta x y z"]]:
+ kpts: Optional[Float[Array, "kpt 3"]] = None
+) -> Float[Array, "kpt beta x y z"]:
"""
Calculate the spherical bessel transform of the beta functions for multiple atoms.
.. math::
- \\beta_l(G) = \\int_0^\infty \\beta(r) j_l(Gr) r^2 dr
+ \beta_l(G) = \int_0^\infty \beta(r) j_l(Gr) r^2 dr
Return the beta function value of angular momentum values :math:`l` at the reciprocal vectors :math:`G` per atom
Args:
- r_grid (List[Float[Array, "r"]]): the r grid corresponding to the beta
- functions.
- nonlocal_beta_grid (List[Float[Array, "beta r"]]): beta values.
- nonlocal_angular_momentum (List[List[int]]): angular momentum corresponding to the beta functions.
- g_vector_grid (Float[Array, "x y z 3"]): reciprocal vectors to interpolate.
+ r_grid (List[Float[Array, "r"]]): the r grid corresponding to the beta
+ functions.
+ nonlocal_beta_grid (List[Float[Array, "beta r"]]): beta values.
+ nonlocal_angular_momentum (List[List[int]]): angular momentum corresponding to the beta functions.
+ g_vector_grid (Float[Array, "x y z 3"]): reciprocal vectors to interpolate.
+ kpts (Optional[Float[Array, "kpt 3"]]): k-points. Default is None.
Returns:
- List[Float[Array, "beta x y z"]]: the beta functions in reciprocal space.
-
- .. warning::
- Cubic spline interpolation is not implemented in JAX. This function uses ``NumPy`` and is not differentiable.
+ Float[Array, "kpt beta x y z"]: An jax.array of the beta functions evaluated in reciprocal space grid.
"""
output = []
for r, b, l in zip(r_grid, nonlocal_beta_grid, nonlocal_angular_momentum):
- output.append(beta_sbt_grid_single_atom(r, b, l, g_vector_grid))
- return jnp.stack(output)
+ output.append(beta_sbt_grid_single_atom(r, b, l, g_vector_grid, kpts))
+ output = np.concatenate(output, axis=1)
+ return output
diff --git a/jrystal/pseudopotential/dataclass.py b/jrystal/pseudopotential/dataclass.py
index e3071f9..3992d26 100644
--- a/jrystal/pseudopotential/dataclass.py
+++ b/jrystal/pseudopotential/dataclass.py
@@ -75,6 +75,8 @@ class NormConservingPseudopotential(Pseudopotential):
r_grid: List[Float[Array, "num_r"]]
r_cutoff: List[float]
+ l_max: int
+ l_max_rho: int
local_potential_grid: List[Float[Array, "num_r"]]
local_potential_charge: List[int]
num_beta: List[int]
@@ -103,6 +105,8 @@ def create(
valence_charges = []
r_grid = []
r_cutoff = []
+ l_max = []
+ l_max_rho = []
local_potential_grid = []
local_potential_charge = []
nonlocal_num_beta = []
@@ -117,33 +121,43 @@ def create(
r_grid.append(np.array(pp["PP_MESH"]["PP_R"]))
# r_cutoff.append(float(pp["PP_NONLOCAL"]["PP_BETA"]["cutoff_radius"][0]))
r_cutoff.append(None)
+ l_max.append(int(pp["PP_HEADER"]["l_max"]))
+ l_max_rho.append(int(pp["PP_HEADER"]["l_max_rho"]))
# norm conserving pseudopotential use the same cutoff_radius for local
# and nonlocal potentials.
local_potential_grid.append(np.array(pp["PP_LOCAL"]))
- local_potential_charge.append(valence_charges[-1] * 2)
- # The factor of 2 is due to the conversion from rydberg to hartree,
- # which is to make it consistent with QE.
+ local_potential_charge.append(valence_charges[-1])
beta = []
- num_beta = len(pp["PP_NONLOCAL"]["PP_BETA"])
- nonlocal_num_beta.append(num_beta)
- beta_angular_momentum = []
- for beta_i in pp["PP_NONLOCAL"]["PP_BETA"]:
- beta.append(beta_i['values'] / r_grid[-1])
- beta_angular_momentum.append(int(beta_i["angular_momentum"]))
-
- nonlocal_beta_grid.append(np.stack(beta))
- nonlocal_beta_cutoff_radius.append(beta_i['cutoff_radius'])
- d_mat = np.array(pp["PP_NONLOCAL"]["PP_DIJ"])
- d_mat = np.reshape(d_mat, [num_beta, num_beta])
- nonlocal_d_matrix.append(d_mat)
+
+ if "PP_BETA" in pp["PP_NONLOCAL"]:
+ num_beta = len(pp["PP_NONLOCAL"]["PP_BETA"])
+ nonlocal_num_beta.append(num_beta)
+ beta_angular_momentum = []
+ for beta_i in pp["PP_NONLOCAL"]["PP_BETA"]:
+ beta.append(
+ np.divide(beta_i['values'], r_grid[-1], where=(r_grid[-1] > 0))
+ ) # the beta function is multiplied by r in upf file
+ beta_angular_momentum.append(int(beta_i["angular_momentum"]))
+ nonlocal_beta_grid.append(np.stack(beta))
+ nonlocal_beta_cutoff_radius.append(beta_i['cutoff_radius'])
+ d_mat = np.array(pp["PP_NONLOCAL"]["PP_DIJ"])
+ d_mat = np.reshape(d_mat, [num_beta, num_beta])
+ nonlocal_d_matrix.append(d_mat)
+
+ else:
+ nonlocal_num_beta.append(1)
+ beta_angular_momentum = [0]
+ beta.append(np.zeros_like(r_grid[-1]))
+
+ nonlocal_beta_grid.append(np.stack(beta))
+ nonlocal_beta_cutoff_radius.append(0)
+ d_mat = np.zeros([1, 1])
+ nonlocal_d_matrix.append(d_mat)
# 1/2 is due to the conversion from rydberg to hartree.
- nonlocal_angular_momentum.append(
- # [int(am) for am in beta_i["angular_momentum"]]
- beta_angular_momentum
- )
+ nonlocal_angular_momentum.append(np.array(beta_angular_momentum))
nonlocal_valence_configuration.append(
pp["PP_INFO"]["Valence configuration"]
)
@@ -156,6 +170,8 @@ def create(
valence_charges,
r_grid,
r_cutoff,
+ l_max,
+ l_max_rho,
local_potential_grid,
local_potential_charge,
nonlocal_num_beta,
@@ -165,3 +181,132 @@ def create(
nonlocal_angular_momentum,
nonlocal_valence_configuration
)
+
+
+@dataclass
+class UltrasoftPseudopotential(NormConservingPseudopotential):
+ """Ultrasoft Pseudopotential Container.
+
+ Attributes:
+ num_atom (int): Number of atoms.
+ positions (np.ndarray): Atom positions.
+ charges (np.ndarray): Atom charges.
+ atomic_symbols (List[str]): Atomic symbols.
+ valence_charges (List[int]): Valence charges.
+ r_grid (List[np.ndarray]): r grid.
+ r_cutoff (List[float]): r cutoff.
+ local_potential_grid (List[np.ndarray]): Local potential grid.
+ local_potential_charge (List[int]): Local potential charge.
+ num_beta (List[int]): Number of beta functions.
+ nonlocal_beta_grid (List[np.ndarray]): Nonlocal beta grid.
+ nonlocal_beta_cutoff_radius (List[List[float]]): Nonlocal beta cutoff
+ radius.
+ nonlocal_d_matrix (List[np.ndarray]): Nonlocal d matrix.
+ nonlocal_angular_momentum (List[List[int]]): Nonlocal angular momentum.
+ nonlocal_valence_configuration (List[List[dict]]): Nonlocal valence
+ configuration.
+
+ nonlocal_q_matrix (List[np.ndarray]): Nonlocal q matrix.
+ nonlocal_augmentation_qij (List[np.ndarray]): Nonlocal augmentation
+ charge.
+
+ Warning: the shape of nonlocal_augmentation_qij depends on the value of
+ `q_with_l` in the UPF file.
+
+ If `q_with_l` is .True., `nonlocal_augmentation_qij` is an array of
+ shape (num_q, num_q, l_max) where `num_q` is the number of augmentation
+ functions and `l_max` is the maximum angular momentum.
+
+ If `q_with_l` is .False., the `nonlocal_augmentation_qij` is an array of
+ shape (num_q, num_q) where `num_q` is the number of augmentation
+ functions.
+ """
+ nonlocal_augmentation_q_matrix: List[np.ndarray]
+ nonlocal_augmentation_qij: List[np.ndarray]
+ nonlocal_augmentation_q_with_l: List[bool]
+
+ @staticmethod
+ def create(
+ crystal: Crystal,
+ dir: Union[str, None] = None,
+ ):
+
+ ncpp = NormConservingPseudopotential.create(crystal, dir)
+
+ pp_dict_list = []
+ for symbol in crystal.symbol:
+ pp_path = find_upf(dir, symbol)
+ pp_dict = parse_upf(pp_path)
+ pp_dict_list.append(pp_dict)
+
+ nonlocal_augmentation_q_matrix = []
+ nonlocal_augmentation_qij = []
+ nonlocal_augmentation_q_with_l = []
+
+ for pp in pp_dict_list:
+ if "PP_AUGMENTATION" in pp["PP_NONLOCAL"]:
+ q_matrix = np.array(pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_Q"])
+ # q_matrix *= 2 * np.sqrt(np.pi)
+ num_q = len(pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_Q"])
+ num_q = np.sqrt(num_q).astype(int)
+ nonlocal_augmentation_q_matrix.append(q_matrix.reshape(num_q, num_q))
+
+ num_r_grid = len(pp["PP_MESH"]["PP_R"])
+ r_grid = np.array(pp["PP_MESH"]["PP_R"])
+ nonlocal_augmentation_q_with_l.append(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["q_with_l"]
+ )
+
+ if nonlocal_augmentation_q_with_l[-1] is True:
+
+ q_ij_a = np.zeros(
+ [num_q, num_q, int(pp["PP_HEADER"]["l_max_rho"]) + 1, num_r_grid]
+ )
+ for i in range(len(pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"])):
+ m = int(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]["first_index"]
+ ) - 1
+ n = int(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]["second_index"]
+ ) - 1
+ angular_momentum = int(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]
+ ["angular_momentum"]
+ )
+ _q_ij = np.array(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]["values"]
+ )
+ q_ij_a[m, n, angular_momentum] = np.array(
+ np.divide(_q_ij, r_grid**2, where=(r_grid > 0))
+ )
+
+ if m != n:
+ q_ij_a[n, m, angular_momentum] = q_ij_a[m, n, angular_momentum]
+
+ else:
+ q_ij_a = np.zeros([num_q, num_q, 1, num_r_grid])
+ for i in range(len(pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"])):
+ m = int(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]["first_index"]
+ ) - 1
+ n = int(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]["second_index"]
+ ) - 1
+ _q_ij = np.array(
+ pp["PP_NONLOCAL"]["PP_AUGMENTATION"]["PP_QIJ"][i]["values"]
+ )
+ q_ij_a[m, n, 0] = np.array(
+ np.divide(_q_ij, r_grid**2, where=(r_grid > 0))
+ )
+
+ if m != n:
+ q_ij_a[n, m, 0] = q_ij_a[m, n, 0]
+
+ nonlocal_augmentation_qij.append(q_ij_a)
+
+ return UltrasoftPseudopotential(
+ **ncpp.__dict__,
+ nonlocal_augmentation_q_matrix=nonlocal_augmentation_q_matrix,
+ nonlocal_augmentation_qij=nonlocal_augmentation_qij,
+ nonlocal_augmentation_q_with_l=nonlocal_augmentation_q_with_l
+ )
diff --git a/jrystal/pseudopotential/interpolate.py b/jrystal/pseudopotential/interpolate.py
deleted file mode 100644
index 77aac7c..0000000
--- a/jrystal/pseudopotential/interpolate.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright 2025 Garena Online Private Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Interpolation Functions"""
-import numpy as np
-from jaxtyping import Float, Array
-from scipy.interpolate import CubicSpline
-
-
-def cubic_spline(
- x: Float[Array, "x"],
- y: Float[Array, "*batch_y x"],
- new_x: Float[Array, "*batch_new_x x"]
-) -> Float[Array, "*batch_y_and_batch_new_x x"]:
- """Interpolate a function evaluated at point :math:`x` with new inputs using cubic spline. Both :math:`y` and new :math:`x` can have batch dimensions. Return the interpolated values, which have the same batch dimensions as :math:`y` and :math:`new_x`. The function is not differentiable.
-
- .. warning::
- Cubic spline interpolation is not implemented in JAX. This function uses ``NumPy`` and is not differentiable.
-
- Args:
- x (Float[Array, "x"]): x values.
- y (Float[Array, "*batch_y x"]): y values.
- new_x (Float[Array, "*batch_new_x x"]): new x values.
-
- Returns:
- Float[Array, "*batch_y *batch_new_x x"]: interpolated y values.
- """
-
- cs = CubicSpline(x, y, axis=-1)
- new_y = np.apply_along_axis(cs, -1, new_x)
- y_batch_dim = y.ndim - 1
- new_y = np.moveaxis(new_y, range(-y_batch_dim - 1, -1), range(0, y_batch_dim))
- return new_y
diff --git a/jrystal/pseudopotential/load.py b/jrystal/pseudopotential/load.py
index c7eaf26..e4b16a6 100644
--- a/jrystal/pseudopotential/load.py
+++ b/jrystal/pseudopotential/load.py
@@ -31,72 +31,103 @@ def extract_value(line: str) -> str:
def parse_pp_info(pp_info_string: str) -> dict:
"""Parse the PP_INFO section into a dictionary."""
lines = pp_info_string.split('\n')
-
- valence_configuration_index = None
- for idx, line in enumerate(lines):
- if "valence configuration" in line.lower():
- valence_configuration_index = idx
- if (
- valence_configuration_index and "generation configuration" in line.lower()
- ):
- valence_configuration_end_index = idx
-
- if valence_configuration_index is None:
- raise ValueError("pseudopotential does not find valence configuration.")
-
parsed_data = {
- 'Generated by':
- extract_value(lines[0]),
- 'Author':
- extract_value(lines[1]),
- 'Generation date':
- extract_value(lines[2]),
- 'Pseudopotential type':
- extract_value(lines[3]),
- 'Element':
- extract_value(lines[4]),
- 'Functional':
- extract_value(lines[5]),
- 'Suggested minimum cutoff for wavefunctions':
- extract_value(lines[6]),
- 'Suggested minimum cutoff for charge density':
- extract_value(lines[7]),
- 'Relativistic Calculation':
- lines[8].strip(),
- 'Local Potential':
- extract_value(lines[9]),
- 'Valence configuration':
- parse_valence_configuration(
- lines[valence_configuration_index + 2:valence_configuration_end_index +
- 1]
- ),
- 'Generation configuration':
- lines[-1].strip()
+ 'Generated by': extract_value(lines[0]),
+ 'Author': extract_value(lines[1]),
+ 'Generation date': extract_value(lines[2]),
+ 'Pseudopotential type': extract_value(lines[3]),
+ 'Element': extract_value(lines[4]),
+ 'Functional': extract_value(lines[5]),
+ 'Suggested minimum cutoff for wavefunctions': extract_value(lines[6]),
+ 'Suggested minimum cutoff for charge density': extract_value(lines[7]),
+ 'Relativistic Calculation': lines[8].strip(),
+ 'Local Potential': extract_value(lines[9]),
+ 'Valence configuration': parse_valence_config(pp_info_string),
+ 'Generation configuration': parse_generation_config(pp_info_string)
}
return parsed_data
-def parse_valence_configuration(valence_lines: list) -> list:
- """Parse the valence configuration section into a list of dictionaries."""
- valence_config = []
+def parse_valence_config(text):
+ lines = text.split('\n')
+ valence_start = -1
+ valence_end = -1
+
+ for i, line in enumerate(lines):
+ if "Valence configuration:" in line:
+ valence_start = i
+ elif valence_start != -1 and (
+ "Generation configuration:" in line or "<" in line
+ ):
+ valence_end = i
+ break
+
+ valence_lines = []
+ if valence_start != -1:
+ if valence_end == -1:
+ valence_end = len(lines)
+ valence_lines = lines[valence_start + 1:valence_end]
+
+ valence_config = {}
for line in valence_lines:
- if line.strip() and not line.startswith('Generation configuration'):
- valence_data = line.split()
- if valence_data:
- valence_entry = {
- 'nl': valence_data[0],
- 'pn': int(valence_data[1]),
- 'l': int(valence_data[2]),
- 'occ': float(valence_data[3]),
- 'Rcut': float(valence_data[4]),
- 'Rcut_US': float(valence_data[5]),
- 'E_pseu': float(valence_data[6])
- }
- valence_config.append(valence_entry)
+ line = line.strip()
+ if not line or line.startswith("nl pn") or line.startswith("--"):
+ continue
+ parts = line.split()
+ if len(parts) >= 7:
+ key = f"{parts[0]}_{parts[1]}_{parts[2]}"
+ valence_config[key] = {
+ "nl": parts[0],
+ "pn": int(parts[1]),
+ "l": int(parts[2]),
+ "occ": float(parts[3]),
+ "Rcut": float(parts[4]),
+ "Rcut US": float(parts[5]),
+ "E pseu": float(parts[6]),
+ }
+
return valence_config
+def parse_generation_config(text):
+ lines = text.split('\n')
+ generation_start = -1
+ generation_end = -1
+
+ for i, line in enumerate(lines):
+ if "Generation configuration:" in line:
+ generation_start = i
+ elif generation_start != -1 and ("<" in line or "" in line):
+ generation_end = i
+ break
+
+ generation_lines = []
+ if generation_start != -1:
+ if generation_end == -1:
+ generation_end = len(lines)
+ generation_lines = lines[generation_start + 1:generation_end]
+
+ generation_config = {}
+ for line in generation_lines:
+ line = line.strip()
+ if not line or line.startswith("nl pn") or line.startswith("--"):
+ continue
+ parts = line.split()
+ if len(parts) >= 7:
+ key = f"{parts[0]}_{parts[1]}_{parts[2]}"
+ generation_config[key] = {
+ "nl": parts[0],
+ "pn": int(parts[1]),
+ "l": int(parts[2]),
+ "occ": float(parts[3]),
+ "Rcut": float(parts[4]),
+ "Rcut US": float(parts[5]),
+ "E pseu": float(parts[6]),
+ }
+ return generation_config
+
+
def parse_pp_header(pp_header) -> dict:
"""Parse the PP_HEADER attributes into a dictionary."""
return {attr: pp_header.get(attr) for attr in pp_header.keys()}
@@ -141,12 +172,15 @@ def parse_pp_nonlocal(pp_nonlocal) -> dict:
augmentation_element = pp_nonlocal.find('PP_AUGMENTATION')
if augmentation_element is not None:
augmentation_data = {
- 'q_with_l': augmentation_element.get('q_with_l') == 'true',
- 'nqf': int(augmentation_element.get('nqf')),
- 'nqlc': int(augmentation_element.get('nqlc')),
+ 'q_with_l':
+ augmentation_element.get('q_with_l') in
+ ['true', 'True', 'TRUE', 'T', 't', '1'],
+ 'nqf':
+ int(augmentation_element.get('nqf')),
+ 'nqlc':
+ int(augmentation_element.get('nqlc')),
}
- # Parse
q_element = augmentation_element.find('PP_Q')
if q_element is not None:
augmentation_data['PP_Q'] = list(map(float, q_element.text.split()))
@@ -163,11 +197,10 @@ def parse_pp_nonlocal(pp_nonlocal) -> dict:
{
'first_index': qij.get('first_index'),
'second_index': qij.get('second_index'),
- 'composite_index': qij.get('composite_index'),
+ 'angular_momentum': qij.get('angular_momentum', 0),
'values': list(map(float, qij.text.split()))
}
)
-
nonlocal_dict['PP_AUGMENTATION'] = augmentation_data
return nonlocal_dict
diff --git a/jrystal/pseudopotential/local.py b/jrystal/pseudopotential/local.py
index ff3cd1e..b620ddc 100644
--- a/jrystal/pseudopotential/local.py
+++ b/jrystal/pseudopotential/local.py
@@ -11,205 +11,155 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-"""Local potential functions for plane waves. """
-import numpy as np
-import jax.numpy as jnp
+"""Local potential functions for plane waves.
+
+This module provides functions for calculating local pseudopotential
+contributions in plane wave basis sets, including reciprocal space
+transformations and Hamiltonian matrix elements.
+"""
from typing import List
-from jaxtyping import Float, Array, Complex
-from .utils import map_over_atoms
-from ..sbt import sbt
+import jax.numpy as jnp
+from interpax import CubicSpline
+from jax.tree_util import tree_map
+from jaxtyping import Array, Complex, Float
+
from .._src import braket
-from .interpolate import cubic_spline
+from ..grid import g2r_vector_grid
-def _potential_local_reciprocal(
+def potential_local_reciprocal(
positions: Float[Array, "atom 3"],
g_vector_grid: Float[Array, "x y z 3"],
r_grid: List[Float[Array, "r"]],
local_potential_grid: List[Float[Array, "r"]],
local_potential_charge: List[int],
vol: float,
-) -> Complex[Array, "x y z"]:
+) -> Float[Array, "x y z"]:
"""Calculate the local potential in reciprocal space.
-
- .. note::
- This function is for split the potential and density from energy calculation such that it is differentiable with respect to the density, and can be jitted.
+
+ This function computes the local pseudopotential contribution in
+ reciprocal space by transforming the real-space local potentials
+ and applying structure factors.
+
+ .. math::
+
+ < v_loc | G >
Args:
- positions (Float[Array, "atom 3"]): The positions of the atoms.
- g_vector_grid (Float[Array, "x y z 3"]): The grid of the reciprocal vectors.
- r_grid (List[Float[Array, "r"]]): The grid of the real space.
- local_potential_grid (List[Float[Array, "r"]]): The local potential in real space.
- local_potential_charge (List[int]): The charge of the local potential.
- vol (float): The volume of the unit cell.
+ positions (Float[Array, "atom 3"]): Atomic positions in Cartesian
+ coordinates.
+ g_vector_grid (Float[Array, "x y z 3"]): Reciprocal space grid vectors.
+ r_grid (List[Float[Array, "r"]]): Real-space radial grids for each atom
+ type.
+ local_potential_grid (List[Float[Array, "r"]]): Local potential values on
+ radial grids.
+ local_potential_charge (List[int]): Nuclear charges for each atom type.
+ vol (float): Unit cell volume.
Returns:
- Complex[Array, "x y z"]: The local potential in reciprocal space.
+ Float[Array, "x y z"]: Local potential in reciprocal space grid.
"""
-
- # v_loc = (v_loc + Z/r) - Z/r where v_loc is always negative
-
- # First part v1 = (v_loc + Z/r)
- # shape: [na, nr]
+ # Convert reciprocal to real space grid
+ r_vector_grid = g2r_vector_grid(g_vector_grid)
+ r_radius = jnp.sqrt(jnp.sum(r_vector_grid**2, axis=-1))
g_radius = jnp.sqrt(jnp.sum(g_vector_grid**2, axis=-1))
num_grids = jnp.prod(jnp.array(g_vector_grid.shape[:-1]))
- local_potential_charge = jnp.array(local_potential_charge)
-
- #############################################################
- # V_r + Z/r - Z/r, where V_r + Z/r is numerical and Z/r is analytical
- @map_over_atoms
- def g(r, v_r, z):
- v_r_prime = v_r + z / r
- kk, f_k = sbt(r, v_r_prime, l=0, kmax=np.max(g_radius), norm=False)
- f_k = cubic_spline(kk, f_k, g_radius)
- f_k *= 4 * np.pi
- return f_k
-
- v1_g = g(r_grid, local_potential_grid, local_potential_charge)
- v1_g = jnp.stack(v1_g) # shape [na x y z]
-
- # Second part v2 = - Z/r in reciprocal space
- g_radius = g_radius.at[0, 0, 0].set(1e10)
- v2_g = jnp.expand_dims(
- local_potential_charge, axis=(1, 2, 3)
- ) / jnp.expand_dims(g_radius**2, 0) # [natom x y z]
+
+ def _g(r, v_r, z):
+ """Com pute the modified potential component for a single atom type."""
+ # Add Coulomb tail: v_r + z/r
+ v_r_modified = v_r + z / r
+ # Interpolate to 3D grid
+ cs = CubicSpline(r, v_r_modified)
+ v_r_3d = cs(r_radius)
+ # Transform to reciprocal space
+ v_g_3d = jnp.fft.fftn(v_r_3d, axes=range(-3, 0)) / num_grids
+ return v_g_3d
+
+ # Compute v1: interpolated local potential in reciprocal space
+ v1_g = tree_map(_g, r_grid, local_potential_grid, local_potential_charge)
+ v1_g = jnp.stack(v1_g) # shape: [na x y z]
+
+ # Compute v2: Coulomb tail correction in reciprocal space
+ # Avoid division by zero at G=0
+ g_radius_safe = g_radius.at[0, 0, 0].set(1e10)
+ charges_expanded = jnp.expand_dims(
+ jnp.array(local_potential_charge), axis=(1, 2, 3)
+ )
+ v2_g = charges_expanded / jnp.expand_dims(g_radius_safe**2, 0)
v2_g *= 4 * jnp.pi
+ # Set G=0 component to zero (neutralizing background)
v2_g = v2_g.at[:, 0, 0, 0].set(0)
- # sum up
+ # Combine v1 and v2 components
v_g = v1_g - v2_g
- v_g = v_g.at[:, 0, 0, 0].set(0)
+ v_g = v_g.at[:, 0, 0, 0].set(0) # Neutralize G=0 component
- ################################################################
- # structure factor
+ # Apply structure factor: exp(-i G · R)
structure_factor = jnp.exp(
-1.j * jnp.matmul(g_vector_grid, positions.transpose())
) # shape: [x y z na]
structure_factor = jnp.transpose(structure_factor, axes=(3, 0, 1, 2))
v_g *= structure_factor
- v_g = jnp.sum(v_g, axis=0) # reduce over atoms
+ v_g = jnp.sum(v_g, axis=0) # Sum over atoms
+ # Apply normalization factors
v_g = v_g * num_grids / vol
- return v_g / 2 # factor of 1/2 is due to the conversion of unit.
+ return v_g
-def _hamiltonian_local(
+def hamiltonian_local(
wave_grid: Complex[Array, "spin kpt band x y z"],
potential_local_grid_reciprocal: Complex[Array, "x y z"],
vol: Float,
) -> Complex[Array, "spin kpt band band"]:
- """The local potential hamiltonian in real space.
-
- .. note::
- This function is for split the potential and density from energy calculation such that it is differentiable with respect to the density, and can be jitted.
-
-
- Args:
- wave_grid (Complex[Array, "spin kpt band x y z"]): The wave function in real space.
- potential_local_grid_reciprocal (Complex[Array, "x y z"]): The local potential in reciprocal space.
- vol (Float): The volume of the unit cell.
+ """Compute the local potential Hamiltonian matrix elements.
- Returns:
- Complex[Array, "spin kpt band band"]: The local potential hamiltonian in real space.
- """
- v_loc_r = jnp.fft.ifftn(potential_local_grid_reciprocal, axes=range(-3, 0))
- hamil = braket.expectation(
- wave_grid, v_loc_r, vol, diagonal=False, mode="real"
- )
- return hamil
+ This function calculates the matrix elements of the local potential
+ operator in the basis of wave functions. The potential is transformed
+ from reciprocal to real space before computing the matrix elements.
-def hamiltonian_local(
- wave_grid: Complex[Array, "spin kpt band x y z"],
- positions: Float[Array, "atom 3"],
- g_vector_grid: Float[Array, "x y z 3"],
- r_grid: Float[Array, "r"],
- local_potential_grid: Float[Array, "r"],
- local_potential_charge: Float[Array, "atom"],
- vol: Float,
-) -> Complex[Array, "spin kpt band band"]:
- """The local potential hamiltonian in reciprocal space.
-
Args:
- wave_grid (Complex[Array, "spin kpt band x y z"]): The wave function in real space.
- positions (Float[Array, "atom 3"]): The positions of the atoms.
- g_vector_grid (Float[Array, "x y z 3"]): The grid of the reciprocal vectors.
- r_grid (Float[Array, "r"]): The grid of the real space.
- local_potential_grid (Float[Array, "r"]): The local potential in real space.
- local_potential_charge (Float[Array, "atom"]): The charge of the local potential.
- vol (Float): The volume of the unit cell.
+ wave_grid: Wave functions in real space grid.
+ potential_local_grid_reciprocal: Local potential in reciprocal space.
+ Can be obtained from :func:`potential_local_reciprocal`.
+ vol: Unit cell volume.
Returns:
- Complex[Array, "spin kpt band band"]: The local potential hamiltonian in reciprocal space.
+ Local potential Hamiltonian matrix elements.
"""
- v_loc_g = _potential_local_reciprocal(
- positions,
- g_vector_grid,
- r_grid,
- local_potential_grid,
- local_potential_charge,
- vol
- )
- v_loc_r = jnp.fft.ifftn(v_loc_g, axes=range(-3, 0))
- hamil = braket.expectation(
- wave_grid, v_loc_r, vol, diagonal=False, mode="real"
+ # Transform potential from reciprocal to real space
+ v_loc_real = jnp.fft.ifftn(potential_local_grid_reciprocal, axes=range(-3, 0))
+ # Compute matrix elements
+ hamiltonian = braket.expectation(
+ wave_grid, v_loc_real, vol, diagonal=False, mode="real"
)
- return hamil
+ return hamiltonian
-def _energy_local(
+def energy_local(
reciprocal_density_grid: Complex[Array, "spin kpt band x y z"],
- v_local_reciprocal: Complex[Array, "x y z"],
+ potential_local_grid_reciprocal: Complex[Array, "x y z"],
vol: Float,
) -> Float:
- """The local potential energy in reciprocal space.
+ """Compute the local potential energy in reciprocal space.
- .. note::
- This function is for split the potential and density from energy calculation such that it is differentiable with respect to the density, and can be jitted.
+ This function calculates the energy contribution from the local potential
+ by computing the inner product between the potential and density in
+ reciprocal space.
Args:
- reciprocal_density_grid (Complex[Array, "spin kpt band x y z"]): The reciprocal density grid.
- v_local_reciprocal (Complex[Array, "x y z"]): The local potential in reciprocal space.
- vol (Float): The volume of the unit cell.
+ reciprocal_density_grid (Complex[Array, "spin kpt band x y z"]): Electron
+ density in reciprocal space.
+ potential_local_grid_reciprocal (Complex[Array, "x y z"]): Local potential
+ in reciprocal space. Can be obtained from `potential_local_reciprocal`.
+ vol (Float): Unit cell volume.
Returns:
- Float: The local potential energy in reciprocal space.
+ Float: Local potential energy contribution.
"""
-
return braket.reciprocal_braket(
- v_local_reciprocal, reciprocal_density_grid, vol
- )
-
-
-def energy_local(
- reciprocal_density_grid: Complex[Array, "spin kpt band x y z"],
- positions: Float[Array, "atom 3"],
- g_vector_grid: Float[Array, "x y z 3"],
- r_grid: Float[Array, "r"],
- local_potential_grid: Float[Array, "r"],
- local_potential_charge: Float[Array, "atom"],
- vol: Float,
-) -> Float:
- """The local potential energy in reciprocal space.
-
- Args:
- reciprocal_density_grid (Complex[Array, "spin kpt band x y z"]): The reciprocal density grid.
- positions (Float[Array, "atom 3"]): The positions of the atoms.
- g_vector_grid (Float[Array, "x y z 3"]): The grid of the reciprocal vectors.
-
- Returns:
- Float: The local potential energy in reciprocal space.
- """
- v_external_reciprocal = _potential_local_reciprocal(
- positions,
- g_vector_grid,
- r_grid,
- local_potential_grid,
- local_potential_charge,
- vol
- )
- external_energy = braket.reciprocal_braket(
- v_external_reciprocal, reciprocal_density_grid, vol
+ potential_local_grid_reciprocal, reciprocal_density_grid, vol
)
- return external_energy.real
diff --git a/jrystal/pseudopotential/nloc.py b/jrystal/pseudopotential/nloc.py
new file mode 100644
index 0000000..a3c0092
--- /dev/null
+++ b/jrystal/pseudopotential/nloc.py
@@ -0,0 +1,222 @@
+"""Nonlocal Pseudopotential. """
+
+import numpy as np
+import jax
+import jax.numpy as jnp
+from functools import partial
+from typing import List, Optional
+from jaxtyping import Float, Array, Complex, Int
+from einops import einsum
+
+from .._src import braket, kinetic
+from .._src import potential, energy
+from .._src.utils import wave_to_density
+from .._src import pw
+from .spherical import legendre_to_sph_harm
+from .local import _hamiltonian_local, _energy_local
+from .beta import beta_sbt_grid_multi_atoms
+
+
+def _potential_nonlocal_square_root(
+ position: Float[Array, "atom 3"],
+ g_vector_grid: Float[Array, "x y z 3"],
+ kpts: Float[Array, "kpt 3"],
+ r_grid: List[Float[Array, "r"]],
+ nonlocal_beta_grid: List[Float[Array, "beta r"]],
+ nonlocal_angular_momentum: List[List[int]],
+ nonlocal_d_matrix: List[Float[Array, "beta beta"]],
+ beta_gk: Float[Array, "kpt beta x y z"] = None
+) -> Complex[Array, "kpt beta phi x y z"]:
+ """
+ Compute the square root of the nonlocal pseudopotential.
+
+ This function returns:
+
+ .. math::
+
+ < \beta_i | (G+k) >
+ """
+ assert len(nonlocal_beta_grid) == len(nonlocal_angular_momentum)
+
+ gk_vector_grid = jnp.expand_dims(
+ kpts, axis=(1, 2, 3)
+ ) + jnp.expand_dims(g_vector_grid, 0) # [nk x y z 3]
+
+ # sbt for beta function and intepolate
+ if beta_gk is None:
+ beta_gk = beta_sbt_grid_multi_atoms(
+ r_grid,
+ nonlocal_beta_grid,
+ nonlocal_angular_momentum,
+ g_vector_grid,
+ kpts
+ ) # [kpt beta x y z]
+
+ assert beta_gk.shape[0] == kpts.shape[0]
+
+ output = []
+ l_max = np.max(np.hstack(nonlocal_angular_momentum))
+
+ for i in range(position.shape[0]):
+ angmom = nonlocal_angular_momentum[i]
+ d_matrix = nonlocal_d_matrix[i]
+ # assert jnp.allclose(d_matrix, d_matrix.T)
+ eigval, eigvec = jnp.linalg.eigh(d_matrix) # shape: [beta beta]
+ d_matrix_sqrt = eigvec * jnp.sqrt(eigval + 0.j) # shape: [beta beta]
+ structure_factor = jnp.exp(
+ -1.j * jnp.matmul(gk_vector_grid, position[i])
+ ) # shape: [nk x y z]
+ kappa_list = []
+ for ln in angmom:
+ kappa_list.append(legendre_to_sph_harm(int(ln), int(l_max)))
+ kappa = []
+ for k in kappa_list:
+ kappa.append(k(gk_vector_grid))
+ kappa = jnp.stack(kappa) # shape: [beta nk x y z phi]
+ kappa = einsum(
+ d_matrix_sqrt,
+ kappa,
+ structure_factor,
+ "b1 b2, b2 k x y z phi, k x y z -> k b1 phi x y z"
+ ) / jnp.sqrt(2) # factor of 1/2 is due to the conversion of unit.
+
+ output.append(kappa)
+
+ output = jnp.concatenate(output, axis=1)
+ output = einsum(
+ output, beta_gk, "k beta phi x y z, k beta x y z -> k beta phi x y z"
+ )
+ return output
+
+
+def _hamiltonian_nonlocal(
+ pw_coefficients: Complex[Array, "spin kpt band x y z"],
+ potential_nl_sqrt: Complex[Array, "kpt beta x y z phi"],
+ vol: Float,
+) -> Complex[Array, "spin kpt band band"]:
+ _f_matrix = einsum(
+ pw_coefficients,
+ potential_nl_sqrt,
+ "s k band x y z, k beta phi x y z -> s k band beta phi"
+ )
+
+ return 4 * jnp.pi / vol * einsum(
+ jnp.conj(_f_matrix),
+ _f_matrix,
+ "s k b1 beta phi, s k b2 beta phi -> s k b1 b2"
+ )
+
+
+def _hamiltonian_matrix(
+ coefficient: Complex[Array, "spin kpoint band *ndim"],
+ hamiltonian_density_grid: Float[Array, "x y z"],
+ potential_local_grid_reciprocal: Float[Array, "r"],
+ potential_nonlocal_grid_sqrt: Complex[Array, "kpt atom beta x y z phi"],
+ g_vector_grid: Float[Array, "x y z 3"],
+ kpts: Float[Array, "num_k 3"],
+ vol: Float,
+ xc: str = 'lda_x',
+ kohn_sham: bool = True
+) -> Complex[Array, "spin kpt band band"]:
+ """
+ Compute the nonlocal pseudopotential hamiltonian.
+
+ Args:
+ coefficient (Complex[Array, "spin kpt band *ndim"]): The plane wave coefficients.
+ hamiltonian_density_grid (ScalarGrid[Float, 3]): The hamiltonian density grid.
+ potential_local_grid_reciprocal (Float[Array, "nr"]): The local potential grid in reciprocal space.
+ potential_nonlocal_grid_sqrt (Complex[Array, "kpt atom beta x y z phi"]): The square root of the nonlocal pseudopotential.
+ g_vector_grid (VectorGrid[Float, 3]): The grid of the reciprocal vectors.
+ kpts (Float[Array, "num_k d"]): The grid of the k-points.
+ """
+
+ dim = kpts.shape[-1]
+ wave_grid = pw.wave_grid(coefficient, vol)
+ ext_nloc = _hamiltonian_nonlocal(
+ coefficient, potential_nonlocal_grid_sqrt, vol
+ )
+ ext_loc = _hamiltonian_local(wave_grid, potential_local_grid_reciprocal, vol)
+
+ hamiltonian_density_grid_reciprocal = jnp.fft.fftn(
+ hamiltonian_density_grid, axes=range(-dim, 0)
+ )
+
+ kin = kinetic.kinetic_operator(g_vector_grid, kpts)
+ h_kin = braket.expectation(
+ coefficient, kin, vol, diagonal=False, mode='kinetic'
+ )
+
+ har = potential.hartree_reciprocal(
+ hamiltonian_density_grid_reciprocal, g_vector_grid, kohn_sham=kohn_sham
+ )
+ har = jnp.fft.ifftn(har, axes=range(-dim, 0))
+ v_xc = potential.xc_density(
+ hamiltonian_density_grid, g_vector_grid, kohn_sham=kohn_sham, xc_type=xc
+ )
+ v_s = har + v_xc
+ h_s = braket.expectation(wave_grid, v_s, vol, diagonal=False, mode="real")
+
+ return ext_nloc + ext_loc + h_s + h_kin
+
+
+def _energy_nonlocal(
+ pw_coefficients: Complex[Array, "spin kpt band x y z"],
+ potential_nl_sqrt: Complex[Array, "kpt beta x y z phi"],
+ vol: Float,
+ occupation: Optional[Float[Array, "spin kpt band"]] = None,
+) -> Float:
+ hamil_nl = _hamiltonian_nonlocal(pw_coefficients, potential_nl_sqrt, vol)
+
+ return jnp.sum(jax.vmap(jax.vmap(jnp.diag))(hamil_nl) * occupation).real
+
+
+def _hamiltonian_trace(
+ coefficient: Complex[Array, "spin kpt band x y z"],
+ hamiltonian_density_grid: Float[Array, "x y z"],
+ potential_local_grid_reciprocal: Float[Array, "r"],
+ potential_nonlocal_grid_sqrt: Complex[Array, "kpt atom beta x y z phi"],
+ g_vector_grid: Float[Array, "x y z 3"],
+ kpts: Float[Array, "kpt 3"],
+ vol: Float,
+ xc: str = 'lda_x',
+ kohn_sham: bool = True
+) -> Float:
+ dim = kpts.shape[-1]
+ wave_grid = pw.wave_grid(coefficient, vol)
+ occupation = jnp.ones(shape=wave_grid.shape[:3], dtype=kpts.dtype)
+
+ density = wave_to_density(wave_grid, occupation)
+ reciprocal_density_grid = jnp.fft.fftn(density, axes=range(-dim, 0))
+
+ ext_nloc = _energy_nonlocal(
+ coefficient, potential_nonlocal_grid_sqrt, vol, occupation
+ )
+ ext_loc = _energy_local(
+ reciprocal_density_grid, potential_local_grid_reciprocal, vol
+ )
+
+ hamiltonian_reciprocal_density_grid = jnp.fft.fftn(
+ hamiltonian_density_grid, axes=range(-dim, 0)
+ )
+
+ v_har_reciprocal = potential.hartree_reciprocal(
+ hamiltonian_reciprocal_density_grid, g_vector_grid, kohn_sham=kohn_sham
+ )
+ v_har = jnp.fft.ifftn(v_har_reciprocal, axes=range(-3, 0))
+ har = braket.expectation(wave_grid, v_har, vol, diagonal=True, mode="real")
+
+ v_xc = potential.xc_density(
+ hamiltonian_density_grid, g_vector_grid, kohn_sham=kohn_sham, xc_type=xc
+ )
+ xc_energy = braket.expectation(
+ wave_grid, v_xc, vol, diagonal=True, mode="real"
+ )
+ h_s = jnp.sum(har + xc_energy)
+
+ t_kin = kinetic.kinetic_operator(g_vector_grid, kpts)
+ kin = braket.expectation(
+ coefficient, t_kin, vol, diagonal=True, mode='kinetic'
+ )
+ kin = jnp.sum(kin)
+
+ return (ext_nloc + ext_loc + h_s + kin).real
diff --git a/jrystal/pseudopotential/normcons.py b/jrystal/pseudopotential/normcons.py
index cefca6e..2fbc6da 100644
--- a/jrystal/pseudopotential/normcons.py
+++ b/jrystal/pseudopotential/normcons.py
@@ -28,6 +28,10 @@
_hamiltonian_local, hamiltonian_local, _energy_local, energy_local
)
from .beta import beta_sbt_grid_multi_atoms
+from .utils import stack_with_padding
+
+from jax.sharding import NamedSharding
+from jax.sharding import PartitionSpec as P
def _potential_nonlocal_square_root(
@@ -36,19 +40,20 @@ def _potential_nonlocal_square_root(
kpts: Float[Array, "kpt 3"],
r_grid: List[Float[Array, "r"]],
nonlocal_beta_grid: List[Float[Array, "beta r"]],
- nonlocal_angular_momentum: List[List[int]]
+ nonlocal_angular_momentum: List[List[int]],
+ sharding: Optional[NamedSharding] = None,
) -> Complex[Array, "kpt atom beta x y z phi"]:
"""
- Compute the square root of the nonlocal pseudopotential.
-
+ Compute the square root of the nonlocal pseudopotential.
+
The Nonlocal pseudopotential hamiltonian is defined by:
.. math::
- < C | V_\\text{nl}(G, G') | C >
+ < C | V_\text{nl}(G, G') | C >
+
+ where :math:`V_\text{nl} = F D F^\dagger`, where :math:`F` can be obtained from this function, and :math:`D` is the diagonal matrix of the beta functions. This function returns :math:`F`.
- where :math:`V_\\text{nl} = F D F^\dagger`, where :math:`F` can be obtained from this function, and :math:`D` is the diagonal matrix of the beta functions. This function returns :math:`F`.
-
Args:
position (Float[Array, "atom 3"]): The positions of the atoms.
g_vector_grid (Float[Array, "x y z 3"]): The grid of the reciprocal vectors.
@@ -66,10 +71,15 @@ def _potential_nonlocal_square_root(
kpts, axis=(1, 2, 3)
) + jnp.expand_dims(g_vector_grid, 0) # [nk x y z 3]
+ mesh = sharding.mesh
+ gk_vector_grid = jax.device_put(gk_vector_grid, NamedSharding(mesh, P("k")))
+
# sbt for beta function and intepolate
beta_gk = beta_sbt_grid_multi_atoms(
r_grid, nonlocal_beta_grid, nonlocal_angular_momentum, gk_vector_grid
) # shape [num_atom num_beta nk x y z]
+ beta_gk = jnp.swapaxes(beta_gk, 0, 2) # shape [nk num_beta natom x y z]
+ beta_gk = jax.device_put(beta_gk, NamedSharding(mesh, P("k")))
# kernel trick for legendre polynormials.
kappa_all = []
@@ -80,24 +90,28 @@ def _potential_nonlocal_square_root(
kappa = []
for k in kappa_list:
kappa.append(k(gk_vector_grid))
- kappa = jnp.stack(kappa) # shape: [num_beta nk x y z dim_phi]
+ kappa = np.stack(kappa) # shape: [beta nk x y z phi]
kappa_all.append(kappa)
- kappa = jnp.stack(kappa_all) # shape: [num_atom num_beta nk x y z dim_phi]
+ kappa = stack_with_padding(kappa_all) # shape: [atom beta nk x y z phi]
+ kappa = np.swapaxes(kappa, 0, 2) # shape: [nk atom beta x y z phi]
+ kappa = jax.device_put(kappa, NamedSharding(mesh, P("k")))
# structure factor
structure_factor = jnp.exp(
- 1.j * jnp.matmul(gk_vector_grid, position.transpose())
+ -1.j * jnp.matmul(gk_vector_grid, position.transpose())
)
# shape: [nk x y z na]
- return einsum(
+ output = einsum(
kappa,
structure_factor,
beta_gk,
- "na nb nk a b c nphi, nk a b c na, na nb nk a b c -> nk na nb a b c nphi"
+ "nk nb na a b c nphi, nk a b c na, nk nb na a b c -> nk na nb a b c nphi"
) / jnp.sqrt(2) # factor of 1/2 is due to the conversion of unit.
+ return output
+
def hamiltonian_nonlocal(
pw_coefficients: Complex[Array, "spin kpt band x y z"],
@@ -141,16 +155,16 @@ def _hamiltonian_nonlocal(
) -> Complex[Array, "spin kpt band band"]:
"""
Compute the nonlocal pseudopotential hamiltonian.
-
+
.. Note::
This function is for seperating the :math:`\sqrt{V_{nl}}` part that may not be tracable by jax.
-
+
Args:
pw_coefficients (Complex[Array, "spin kpt band x y z"]): The plane wave coefficients.
potential_nl_sqrt (Complex[Array, "kpt atom beta x y z phi"]): The square root of the nonlocal pseudopotential.
nonlocal_d_matrix (Float[Array, "j j"]): The diagonal matrix of the beta functions.
vol (Float): The volume of the unit cell.
-
+
Returns:
Complex[Array, "spin kpt band band"]: The nonlocal pseudopotential hamiltonian.
"""
@@ -163,7 +177,7 @@ def _hamiltonian_nonlocal(
return 4 * jnp.pi / vol * einsum(
jnp.conj(_f_matrix),
- jnp.stack(nonlocal_d_matrix),
+ stack_with_padding(nonlocal_d_matrix),
_f_matrix,
"ns nk b1 na j1 nphi, na j1 j2, ns nk b2 na j2 nphi -> ns nk b1 b2"
)
@@ -178,17 +192,17 @@ def _energy_nonlocal(
) -> Float:
"""
Compute the nonlocal pseudopotential energy.
-
+
.. Note::
This function is for seperating the :math:`\sqrt{V_{nl}}` part that may not be tracable by jax.
-
+
Args:
pw_coefficients (Complex[Array, "spin kpt band x y z"]): The plane wave coefficients.
potential_nl_sqrt (Complex[Array, "kpt atom beta x y z phi"]): The square root of the nonlocal pseudopotential.
nonlocal_d_matrix (Float[Array, "j j"]): The diagonal matrix of the beta functions.
vol (Float): The volume of the unit cell.
occupation (Optional[OccupationArray]): The occupation of the states.
-
+
Returns:
Float: The nonlocal pseudopotential energy.
"""
@@ -214,7 +228,7 @@ def energy_nonlocal(
) -> Float:
"""
Compute the nonlocal pseudopotential energy.
-
+
Args:
pw_coefficients (Complex[Array, "spin kpt band x y z"]): The plane wave coefficients.
positions (Float[Array, "atom 3"]): The positions of the atoms.
@@ -225,7 +239,7 @@ def energy_nonlocal(
Returns:
Float: The nonlocal pseudopotential energy.
-
+
"""
hamil_nl = hamiltonian_nonlocal(
pw_coefficients,
@@ -250,12 +264,12 @@ def _hamiltonian_matrix(
kpts: Float[Array, "num_k 3"],
nonlocal_d_matrix: List[Float[Array, "beta beta"]],
vol: Float,
- xc: str = 'lda',
- kohn_sham: bool = False
+ xc: str = 'lda_x',
+ kohn_sham: bool = True
):
"""
Compute the nonlocal pseudopotential hamiltonian.
-
+
Args:
coefficient (Complex[Array, "spin kpt band *ndim"]): The plane wave coefficients.
hamiltonian_density_grid (ScalarGrid[Float, 3]): The hamiltonian density grid.
@@ -282,10 +296,10 @@ def _hamiltonian_matrix(
)
har = potential.hartree_reciprocal(
- hamiltonian_density_grid_reciprocal, g_vector_grid
+ hamiltonian_density_grid_reciprocal, g_vector_grid, kohn_sham=kohn_sham
)
har = jnp.fft.ifftn(har, axes=range(-dim, 0))
- lda = potential.xc_lda(hamiltonian_density_grid)
+ lda = potential.xc_lda(hamiltonian_density_grid, kohn_sham=kohn_sham)
v_s = har + lda
h_s = braket.expectation(wave_grid, v_s, vol, diagonal=False, mode="real")
@@ -307,8 +321,8 @@ def hamiltonian_matrix(
nonlocal_d_matrix: List[Float[Array, "beta beta"]],
nonlocal_angular_momentum: List[List[int]],
vol: Float,
- xc: str = 'lda',
- kohn_sham: bool = False
+ xc: str = 'lda_x',
+ kohn_sham: bool = True
):
dim = positions.shape[-1]
wave_grid = pw.wave_grid(coefficient, vol)
@@ -344,10 +358,10 @@ def hamiltonian_matrix(
)
har = potential.hartree_reciprocal(
- hamiltonian_reciprocal_density_grid, g_vector_grid
+ hamiltonian_reciprocal_density_grid, g_vector_grid, kohn_sham=kohn_sham
)
har = jnp.fft.ifftn(har, axes=range(-dim, 0))
- lda = potential.xc_lda(hamiltonian_density_grid)
+ lda = potential.xc_lda(hamiltonian_density_grid, kohn_sham=kohn_sham)
v_s = har + lda
h_s = braket.expectation(wave_grid, v_s, vol, diagonal=False, mode="real")
@@ -363,6 +377,7 @@ def _hamiltonian_trace(
kpts: Float[Array, "kpt 3"],
nonlocal_d_matrix: List[Float[Array, "beta beta"]],
vol: Float,
+ kohn_sham: bool = True
) -> Float:
dim = kpts.shape[-1]
wave_grid = pw.wave_grid(coefficient, vol)
@@ -387,12 +402,12 @@ def _hamiltonian_trace(
)
v_har_reciprocal = potential.hartree_reciprocal(
- hamiltonian_reciprocal_density_grid, g_vector_grid
+ hamiltonian_reciprocal_density_grid, g_vector_grid, kohn_sham=kohn_sham
)
v_har = jnp.fft.ifftn(v_har_reciprocal, axes=range(-3, 0))
har = braket.expectation(wave_grid, v_har, vol, diagonal=True, mode="real")
- # har = 0
- v_lda = potential.xc_lda(hamiltonian_density_grid)
+
+ v_lda = potential.xc_lda(hamiltonian_density_grid, kohn_sham=kohn_sham)
lda = braket.expectation(wave_grid, v_lda, vol, diagonal=True, mode="real")
h_s = jnp.sum(har + lda)
@@ -419,10 +434,11 @@ def hamiltonian_trace(
nonlocal_d_matrix: List[Float[Array, "beta beta"]],
nonlocal_angular_momentum: List[List[int]],
vol: Float,
+ kohn_sham: bool = True
):
"""
Compute the trace of the hamiltonian.
-
+
Args:
coefficient (Complex[Array, "spin kpt band x y z"]): The plane wave coefficients.
hamiltonian_density_grid (ScalarGrid[Float, 3]): The hamiltonian density grid.
@@ -453,7 +469,6 @@ def hamiltonian_trace(
)
ext_nloc = energy_nonlocal(
coefficient,
- occupation,
positions,
g_vector_grid,
kpts,
@@ -481,7 +496,7 @@ def hamiltonian_trace(
v_har = jnp.fft.ifftn(v_har_reciprocal, axes=range(-3, 0))
har = braket.expectation(wave_grid, v_har, vol, diagonal=True, mode="real")
- v_lda = potential.xc_lda(hamiltonian_density_grid)
+ v_lda = potential.xc_lda(hamiltonian_density_grid,)
lda = braket.expectation(wave_grid, v_lda, vol, diagonal=True, mode="real")
h_s = jnp.sum(har + lda)
kin = energy.kinetic(g_vector_grid, kpts, coefficient, occupation)
diff --git a/jrystal/pseudopotential/spherical.py b/jrystal/pseudopotential/spherical.py
index a3b11f4..fe55d17 100644
--- a/jrystal/pseudopotential/spherical.py
+++ b/jrystal/pseudopotential/spherical.py
@@ -15,11 +15,13 @@
This module provides functions for working with spherical coordinates and spherical harmonics, particularly useful for quantum mechanical calculations and pseudopotential transformations.
"""
+import numpy as np
import jax
import jax.numpy as jnp
-from jax.scipy.special import sph_harm
-from jaxtyping import Float, Array
+from jax.scipy.special import sph_harm, sph_harm_y
+from jaxtyping import Float, Array, Int
from typing import Callable
+from functools import partial
from .._src.utils import vmapstack
@@ -28,7 +30,7 @@ def cartesian_to_spherical(x: Float[Array, "*n 3"],
"""Convert Cartesian coordinates to spherical coordinates.
Transforms 3D Cartesian coordinates (x, y, z) to spherical coordinates (r, θ, φ), where:
-
+
- r is the radial distance from the origin
- θ (theta) is the azimuthal angle in the x-y plane from the x-axis (0 ≤ θ < 2π)
- φ (phi) is the polar angle from the z-axis (0 ≤ φ ≤ π)
@@ -45,17 +47,52 @@ def cartesian_to_spherical(x: Float[Array, "*n 3"],
r = jnp.where(r == 0., eps, r)
# Polar angle (phi)
- phi = jnp.arccos(jnp.clip(x[2] / r, -1.0, 1.0))
+ phi = jnp.arccos(jnp.clip(x[..., 2] / r, -1.0, 1.0))
# Azimuthal angle (theta) and shift to range [0, 2*pi)
- theta = jnp.arctan2(x[1], x[0])
+ theta = jnp.arctan2(x[..., 1], x[..., 0])
theta = jnp.mod(theta + 2 * jnp.pi, 2 * jnp.pi)
return jnp.stack((r, theta, phi), axis=-1)
+def batched_sph_harm(
+ l: int,
+ theta: Float[Array, "*batch"],
+ phi: Float[Array, "*batch"],
+) -> Float[Array, "*batch m"]:
+ """
+ Compute the spherical harmonics for a batch of points.
+
+ this function is used to compute the sum of spherical harmonics:
+
+ .. math::
+
+ Y_{l, m}(theta, phi)
+
+ Args:
+ l (int): The angular momentum quantum number.
+ theta (Float[Array, "*batch"]): The azimuthal angle.
+ phi (Float[Array, "*batch"]): The polar angle.
+
+ Returns:
+ Float[Array, "*batch m"]: The spherical harmonics, where the last dimension is the magnetic quantum number.
+
+ """
+ dim = theta.ndim
+ m = np.arange(-int(l), int(l) + 1)
+ n = np.array([l])
+
+ @vmapstack(dim)
+ def _sph_harm_fun(theta, phi):
+ return sph_harm_y(n, m, phi, theta)
+
+ return _sph_harm_fun(theta, phi)
+
+
def legendre_to_sph_harm(
l: int = 0,
+ l_max: int = 4
) -> Callable[[Float[Array, "*batch 3"]], Float[Array, "*batch m"]]:
"""Convert Legendre polynomials to spherical harmonics decomposition.
@@ -65,7 +102,7 @@ def legendre_to_sph_harm(
(2l+1) P_l(x^Ty) = 4\pi \sum_m Y_{l, m}(x) Y^*_{l, m}(y)
where:
-
+
- l is the angular momentum quantum number
- P_l is the Legendre polynomial of order l
- Y_{l,m} are the spherical harmonics
@@ -75,13 +112,14 @@ def legendre_to_sph_harm(
Args:
l (int): Angular momentum quantum number (default: 0)
+ l_max (int): Maximum angular momentum quantum number to compute (default: 4)
+ The output will be padded to shape
Returns:
- Callable that takes Cartesian coordinates of shape (*batch, 3) and returns spherical harmonics coefficients of shape (*batch, m), where m = 2l+1
+ A Callable function that takes Cartesian coordinates of shape (*batch, 3) and returns spherical harmonics coefficients of shape (*batch, m), where m = 2l+1
"""
- m = jnp.arange(-l, l + 1)
- n = jnp.array([l])
- L_MAX = 4
+ m = np.arange(-l, l + 1)
+ n = np.array([l])
def fun(x):
@@ -95,10 +133,43 @@ def _f(x_i):
sph_harm, in_axes=[0, None, None, None]
)(m, n, theta, phi).reshape([-1]) # [m]
- y_lm = jnp.pad(y_lm, (0, (L_MAX - l) * 2), constant_values=0)
+ y_lm = jnp.pad(y_lm, (0, (l_max - l) * 2), constant_values=0)
# pad the y_lm to length l_max with zeros
return y_lm * 2 * jnp.sqrt(jnp.pi) # [m]
-
return _f(x)
-
return fun
+
+
+def legendre_kernel_trick(l: int = 0) -> Callable: # noqa
+ """Decompose legendre polynomials via kernel trick:
+
+ (2l+1) P_l (x^Ty) = \phi(x)^T \phi(y)
+
+ Return \phi
+
+ Args:
+ l (int, optional): The degree of the legendre polynomial. Defaults to 0.
+
+ Returns:
+ callable: return a function that map from shape [n1 n2 n3 3] to
+ [n1 n2 n3 new_dim] where new_dim is decided using kernel trick.
+ """
+ if l == 0:
+
+ def phi(x):
+ return jnp.sqrt(3) / 3 + jnp.zeros_like(x)
+
+ return phi
+ elif l == 1:
+
+ def phi(x):
+ x = x / jnp.sqrt(jnp.sum(x**2, axis=-1, keepdims=True))
+ x = jnp.where(jnp.isnan(x), 0, x)
+ return jnp.sqrt(3) * x
+
+ # sqrt(3) is due to the (2l+1) factor
+ return phi
+ else:
+ raise NotImplementedError(
+ "The decomposition of legendre has not been implemented for l > 1."
+ )
diff --git a/jrystal/pseudopotential/utils.py b/jrystal/pseudopotential/utils.py
index d739279..aeb42e5 100644
--- a/jrystal/pseudopotential/utils.py
+++ b/jrystal/pseudopotential/utils.py
@@ -14,6 +14,9 @@
from functools import wraps
from typing import List, Callable, Any
+import jax
+import numpy as np
+import jax.numpy as jnp
def map_over_atoms(fun: Callable[..., Any]) -> Callable[..., List]:
@@ -42,3 +45,36 @@ def map_fun(*arrays, **kwargs):
return output
return map_fun
+
+
+def stack_with_padding(array_list: List[List]):
+ # Input validation
+ if not array_list:
+ raise ValueError("Input list is empty!")
+
+ # Verify all arrays have same number of dimensions
+ ndim = array_list[0].ndim
+ for arr in array_list:
+ if arr.ndim != ndim:
+ raise ValueError("All arrays must have the same number of dimensions!")
+
+ # Compute maximum length for each dimension
+ max_shape = np.max([arr.shape for arr in array_list], axis=0)
+
+ # Pad each array only where dimensions don't match
+ padded_arrays = []
+ for arr in array_list:
+ # Determine padding for each axis:
+ # - (0, pad_size) for mismatched axes
+ # - (0, 0) for already matching axes
+ pad_width = []
+ for axis in range(ndim):
+ if arr.shape[axis] != max_shape[axis]:
+ pad_width.append((0, max_shape[axis] - arr.shape[axis]))
+ else:
+ pad_width.append((0, 0))
+ padded_arr = np.pad(arr, pad_width, mode='constant', constant_values=0)
+ padded_arrays.append(padded_arr)
+
+ # Stack all arrays
+ return np.stack(padded_arrays)
diff --git a/main.py b/main.py
index 8edfdb2..52b05d9 100644
--- a/main.py
+++ b/main.py
@@ -1,38 +1,65 @@
import argparse
+
import jrystal as jr
-parser = argparse.ArgumentParser(
- prog='Jrystal', description='Command for Jrystal package.'
-)
-
-parser.add_argument(
- "-m",
- "--mode",
- choices=["energy", "band"],
- default='energy',
- help="Set the computation mode. For total enrgy minimization, please use "
- "\'energy\'. For band structure calculation, please use \'band\'. "
-)
-
-parser.add_argument(
- "-c",
- "--config",
- default='config.yaml',
- help="Set the configuration file path."
-)
-
-args = parser.parse_args()
-
-config = jr.config.get_config("config.yaml")
-
-if args.mode == "energy":
- if config.use_pseudopotential:
- jr.calc.energy_normcons(config)
- else:
- jr.calc.energy(config)
-elif args.mode == "band":
- if config.use_pseudopotential:
- jr.calc.band_normcons(config)
- else:
- jr.calc.band(config)
+def main():
+ parser = argparse.ArgumentParser(
+ prog='Jrystal', description='Command for Jrystal package.'
+ )
+
+ parser.add_argument(
+ "-m",
+ "--mode",
+ choices=["energy", "band"],
+ default='energy',
+ help="Set the computation mode. For total enrgy minimization, please use "
+ "\'energy\'. For band structure calculation, please use \'band\'. "
+ )
+
+ parser.add_argument(
+ "-c",
+ "--config",
+ default='config.yaml',
+ help="Set the configuration file path."
+ )
+
+ parser.add_argument(
+ "-l",
+ "--load",
+ help="Load pickled output from energy calculation for band structure calculation."
+ )
+
+ args = parser.parse_args()
+
+ config = jr.config.get_config("config.yaml")
+
+ if args.mode == "energy":
+ if config.use_pseudopotential:
+ if config.pseudopotential_type == "normcons":
+ jr.calc.energy_normcons(config)
+ elif config.pseudopotential_type == "ultrasoft":
+ jr.calc.energy_ultrasoft(config)
+ else:
+ raise ValueError(
+ f"Pseudopotential {config.pseudopotential_type} is not supported."
+ )
+ else:
+ jr.calc.energy_all_electrons(config)
+
+ elif args.mode == "band":
+ if config.use_pseudopotential:
+ if config.pseudopotential_type == "normcons":
+ jr.calc.band_normcons(config)
+ elif config.pseudopotential_type == "ultrasoft":
+ jr.calc.band_ultrasoft(config)
+ else:
+ raise ValueError(
+ f"Pseudopotential {config.pseudopotential_type} is not supported."
+ )
+ else:
+ jr.calc.band_all_electrons(config)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pseudopotential/normconserving/Al.pz-vbc.UPF b/pseudopotential/normcons/Al.pz-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Al.pz-vbc.UPF
rename to pseudopotential/normcons/Al.pz-vbc.UPF
diff --git a/pseudopotential/normconserving/As.pz-bhs.UPF b/pseudopotential/normcons/As.pz-bhs.UPF
similarity index 100%
rename from pseudopotential/normconserving/As.pz-bhs.UPF
rename to pseudopotential/normcons/As.pz-bhs.UPF
diff --git a/pseudopotential/normconserving/B.pz-vbc.UPF b/pseudopotential/normcons/B.pz-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/B.pz-vbc.UPF
rename to pseudopotential/normcons/B.pz-vbc.UPF
diff --git a/pseudopotential/normconserving/Be.pz-n-vbc.UPF b/pseudopotential/normcons/Be.pz-n-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Be.pz-n-vbc.UPF
rename to pseudopotential/normcons/Be.pz-n-vbc.UPF
diff --git a/pseudopotential/normconserving/C.pz-vbc.UPF b/pseudopotential/normcons/C.pz-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/C.pz-vbc.UPF
rename to pseudopotential/normcons/C.pz-vbc.UPF
diff --git a/pseudopotential/normconserving/Ca.pz-n-vbc.UPF b/pseudopotential/normcons/Ca.pz-n-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Ca.pz-n-vbc.UPF
rename to pseudopotential/normcons/Ca.pz-n-vbc.UPF
diff --git a/pseudopotential/normconserving/Cl.pz-bhs.UPF b/pseudopotential/normcons/Cl.pz-bhs.UPF
similarity index 100%
rename from pseudopotential/normconserving/Cl.pz-bhs.UPF
rename to pseudopotential/normcons/Cl.pz-bhs.UPF
diff --git a/pseudopotential/normconserving/Ga.pz-bhs.UPF b/pseudopotential/normcons/Ga.pz-bhs.UPF
similarity index 100%
rename from pseudopotential/normconserving/Ga.pz-bhs.UPF
rename to pseudopotential/normcons/Ga.pz-bhs.UPF
diff --git a/pseudopotential/normconserving/Ge.pz-bhs.UPF b/pseudopotential/normcons/Ge.pz-bhs.UPF
similarity index 100%
rename from pseudopotential/normconserving/Ge.pz-bhs.UPF
rename to pseudopotential/normcons/Ge.pz-bhs.UPF
diff --git a/pseudopotential/normconserving/H.pz-vbc.UPF b/pseudopotential/normcons/H.pz-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/H.pz-vbc.UPF
rename to pseudopotential/normcons/H.pz-vbc.UPF
diff --git a/pseudopotential/normconserving/He.pz-mt.UPF b/pseudopotential/normcons/He.pz-mt.UPF
similarity index 100%
rename from pseudopotential/normconserving/He.pz-mt.UPF
rename to pseudopotential/normcons/He.pz-mt.UPF
diff --git a/pseudopotential/normcons/I.pbe-mt_bw.UPF b/pseudopotential/normcons/I.pbe-mt_bw.UPF
new file mode 100644
index 0000000..373aaf4
--- /dev/null
+++ b/pseudopotential/normcons/I.pbe-mt_bw.UPF
@@ -0,0 +1,1293 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: NC
+Element: I
+Functional: SLA PW PBE PBE
+Suggested minimum cutoff for wavefunctions: 0. Ry
+Suggested minimum cutoff for charge density: 0. Ry
+The Pseudo was generated with a Scalar-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+5S 0 0 2.00 0.000 0.000 0.000000
+5P 0 1 5.00 0.000 0.000 0.000000
+5D 0 2 0.00 0.000 0.000 0.000000
+Generation configuration: not available.
+
+
+
+
+
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diff --git a/pseudopotential/normconserving/K.pz-n-vbc.UPF b/pseudopotential/normcons/K.pz-n-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/K.pz-n-vbc.UPF
rename to pseudopotential/normcons/K.pz-n-vbc.UPF
diff --git a/pseudopotential/normconserving/Li.pz-n-vbc.UPF b/pseudopotential/normcons/Li.pz-n-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Li.pz-n-vbc.UPF
rename to pseudopotential/normcons/Li.pz-n-vbc.UPF
diff --git a/pseudopotential/normconserving/Mg.pz-n-vbc.UPF b/pseudopotential/normcons/Mg.pz-n-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Mg.pz-n-vbc.UPF
rename to pseudopotential/normcons/Mg.pz-n-vbc.UPF
diff --git a/pseudopotential/normconserving/N.pz-vbc.UPF b/pseudopotential/normcons/N.pz-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/N.pz-vbc.UPF
rename to pseudopotential/normcons/N.pz-vbc.UPF
diff --git a/pseudopotential/normconserving/Na.pz-n-vbc.UPF b/pseudopotential/normcons/Na.pz-n-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Na.pz-n-vbc.UPF
rename to pseudopotential/normcons/Na.pz-n-vbc.UPF
diff --git a/pseudopotential/normconserving/O.pz-mt.UPF b/pseudopotential/normcons/O.pz-mt.UPF
similarity index 100%
rename from pseudopotential/normconserving/O.pz-mt.UPF
rename to pseudopotential/normcons/O.pz-mt.UPF
diff --git a/pseudopotential/normconserving/P.pz-bhs.UPF b/pseudopotential/normcons/P.pz-bhs.UPF
similarity index 100%
rename from pseudopotential/normconserving/P.pz-bhs.UPF
rename to pseudopotential/normcons/P.pz-bhs.UPF
diff --git a/pseudopotential/normcons/Pb.pz-bhs.UPF b/pseudopotential/normcons/Pb.pz-bhs.UPF
new file mode 100644
index 0000000..ef87a75
--- /dev/null
+++ b/pseudopotential/normcons/Pb.pz-bhs.UPF
@@ -0,0 +1,885 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: NC
+Element: Pb
+Functional: SLA PZ NOGX NOGC
+Suggested minimum cutoff for wavefunctions: 0. Ry
+Suggested minimum cutoff for charge density: 0. Ry
+The Pseudo was generated with a Scalar-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+6S 0 0 2.00 0.000 0.000 0.000000
+6P 0 1 2.00 0.000 0.000 0.000000
+6D 0 2 0.00 0.000 0.000 0.000000
+Generation configuration: not available.
+
+
+
+
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diff --git a/pseudopotential/normconserving/S.pz-bhs.UPF b/pseudopotential/normcons/S.pz-bhs.UPF
similarity index 100%
rename from pseudopotential/normconserving/S.pz-bhs.UPF
rename to pseudopotential/normcons/S.pz-bhs.UPF
diff --git a/pseudopotential/normconserving/Si.pz-vbc.UPF b/pseudopotential/normcons/Si.pz-vbc.UPF
similarity index 100%
rename from pseudopotential/normconserving/Si.pz-vbc.UPF
rename to pseudopotential/normcons/Si.pz-vbc.UPF
diff --git a/pseudopotential/normcons/_H.pz-vbc_150.UPF b/pseudopotential/normcons/_H.pz-vbc_150.UPF
new file mode 100644
index 0000000..6e93190
--- /dev/null
+++ b/pseudopotential/normcons/_H.pz-vbc_150.UPF
@@ -0,0 +1,206 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: NC
+Element: H
+Functional: SLA PZ NOGX NOGC
+Suggested minimum cutoff for wavefunctions: 0. Ry
+Suggested minimum cutoff for charge density: 0. Ry
+The Pseudo was generated with a Non-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+1S 0 0 1.00 0.000 0.000 0.000000
+Generation configuration: not available.
+
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diff --git a/pseudopotential/ultrasoft/C.pbe-rrkjus.UPF b/pseudopotential/ultrasoft/C.pbe-rrkjus.UPF
new file mode 100644
index 0000000..33f80d8
--- /dev/null
+++ b/pseudopotential/ultrasoft/C.pbe-rrkjus.UPF
@@ -0,0 +1,3223 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: US
+Element: C
+Functional: SLA PW PBE PBE
+Suggested minimum cutoff for wavefunctions: 0. Ry
+Suggested minimum cutoff for charge density: 0. Ry
+The Pseudo was generated with a Non-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+2S 0 0 2.00 1.200 1.600 0.000000
+2P 0 1 2.00 1.200 1.600 0.000000
+Generation configuration: not available.
+
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diff --git a/pseudopotential/ultrasoft/Na.pz-sp-van_ak.UPF b/pseudopotential/ultrasoft/Na.pz-sp-van_ak.UPF
new file mode 100644
index 0000000..c95f58b
--- /dev/null
+++ b/pseudopotential/ultrasoft/Na.pz-sp-van_ak.UPF
@@ -0,0 +1,7459 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: US
+Element: Na
+Functional: SLA PZ NOGX NOGC
+Suggested minimum cutoff for wavefunctions: 0. Ry
+Suggested minimum cutoff for charge density: 0. Ry
+The Pseudo was generated with a Non-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+2S 0 0 2.00 0.900 1.800 0.000000
+2P 0 1 6.00 0.900 1.800 0.000000
+3S 0 0 1.00 0.900 1.800 0.000000
+Generation configuration: not available.
+
+
+
+
+
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diff --git a/pseudopotential/ultrasoft/Si.pbe-van_gipaw.UPF b/pseudopotential/ultrasoft/Si.pbe-van_gipaw.UPF
new file mode 100644
index 0000000..926dfff
--- /dev/null
+++ b/pseudopotential/ultrasoft/Si.pbe-van_gipaw.UPF
@@ -0,0 +1,11009 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: US
+Element: Si
+Functional: SLA PW PBX PBC
+Suggested minimum cutoff for wavefunctions: 18. Ry
+Suggested minimum cutoff for charge density: 249. Ry
+The Pseudo was generated with a Non-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Pseudopotential contains additional information for GIPAW reconstruction.
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+3S 0 0 2.00 2.200 2.400 0.000000
+4S 0 0 0.00 1.400 1.600 0.000000
+3P 0 1 1.00 2.200 2.400 0.000000
+4P 0 1 0.00 2.200 2.400 0.000000
+3D 0 2 1.00 0.000 0.000 0.000000
+Generation configuration: not available.
+
+
+
+
+
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diff --git a/pseudopotential/ultrasoft/_C.pbe-rrkjus_normcons.UPF b/pseudopotential/ultrasoft/_C.pbe-rrkjus_normcons.UPF
new file mode 100644
index 0000000..7160ae8
--- /dev/null
+++ b/pseudopotential/ultrasoft/_C.pbe-rrkjus_normcons.UPF
@@ -0,0 +1,864 @@
+
+
+Generated by new atomic code, or converted to UPF format
+Author:
+Generation date:
+Pseudopotential type: US
+Element: C
+Functional: SLA PW PBE PBE
+Suggested minimum cutoff for wavefunctions: 0. Ry
+Suggested minimum cutoff for charge density: 0. Ry
+The Pseudo was generated with a Non-Relativistic Calculation
+L component and cutoff radius for Local Potential: 0 0.0000
+Valence configuration:
+nl pn l occ Rcut Rcut US E pseu
+2S 0 0 2.00 1.200 1.600 0.000000
+2P 0 1 2.00 1.200 1.600 0.000000
+Generation configuration: not available.
+
+
+
+
+
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diff --git a/pseudopotential/ultrasoft/new_C.pbe-n-rrkjus_psl.1.0.0.UPF b/pseudopotential/ultrasoft/new_C.pbe-n-rrkjus_psl.1.0.0.UPF
new file mode 100644
index 0000000..7cc98f6
--- /dev/null
+++ b/pseudopotential/ultrasoft/new_C.pbe-n-rrkjus_psl.1.0.0.UPF
@@ -0,0 +1,8493 @@
+
+
+Generated using "atomic" code by A. Dal Corso v.6.3MaX
+Author: ADC
+Generation date: 4Sep2018
+Pseudopotential type: USPP
+Element: C
+Functional: PBE
+ Suggested minimum cutoff for wavefunctions: 40. Ry
+ Suggested minimum cutoff for charge density: 326. Ry
+ The Pseudo was generated with a Scalar-Relativistic Calculation
+ Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.0000
+ Pseudopotential contains additional information for GIPAW reconstruction.
+ Valence configuration:
+ nl pn l occ Rcut Rcut US E pseu
+ 2S 1 0 2.00 1.000 1.200 -1.010678
+ 2P 2 1 2.00 0.900 1.400 -0.388489
+ Generation configuration:
+ 2S 1 0 2.00 1.000 1.200 -1.010669
+ 2S 1 0 0.00 1.000 1.200 3.000000
+ 2P 2 1 2.00 0.900 1.400 -0.388487
+ 2P 2 1 0.00 0.900 1.400 0.050000
+ Pseudization used: troullier-martins
+
+
+ &input
+ title='C',
+ zed=6.,
+ rel=1,
+ config='[He] 2s2 2p2',
+ iswitch=3,
+ dft='PBE'
+ /
+ &inputp
+ lpaw=.false.,
+ use_xsd=.FALSE.,
+ pseudotype=3,
+ file_pseudopw='C.pbe-n-rrkjus_psl.1.0.0.UPF',
+ author='ADC',
+ lloc=-1,
+ rcloc=1.0
+ which_augfun='PSQ',
+ rmatch_augfun_nc=.true.,
+ nlcc=.true.,
+ new_core_ps=.true.,
+ rcore=0.7,
+ tm=.true.
+ /
+4
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+
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diff --git a/pseudopotential/ultrasoft/new_Si.pbe-nl-rrkjus_psl.1.0.0.UPF b/pseudopotential/ultrasoft/new_Si.pbe-nl-rrkjus_psl.1.0.0.UPF
new file mode 100644
index 0000000..1967580
--- /dev/null
+++ b/pseudopotential/ultrasoft/new_Si.pbe-nl-rrkjus_psl.1.0.0.UPF
@@ -0,0 +1,9885 @@
+
+
+Generated using "atomic" code by A. Dal Corso v.6.3
+Author: ADC
+Generation date: 4Sep2018
+Pseudopotential type: USPP
+Element: Si
+Functional: PBE
+ Suggested minimum cutoff for wavefunctions: 44. Ry
+ Suggested minimum cutoff for charge density: 175. Ry
+ The Pseudo was generated with a Scalar-Relativistic Calculation
+ L component and cutoff radius for Local Potential: 2 1.7000
+ Pseudopotential contains additional information for GIPAW reconstruction.
+ Valence configuration:
+ nl pn l occ Rcut Rcut US E pseu
+ 3S 1 0 2.00 1.600 1.800 -0.794728
+ 3P 2 1 2.00 1.600 1.800 -0.299965
+ Generation configuration:
+ 3S 1 0 2.00 1.600 1.800 -0.794725
+ 3S 1 0 0.00 1.600 1.800 6.000000
+ 3P 2 1 2.00 1.600 1.800 -0.299965
+ 3P 2 1 0.00 1.600 1.800 6.000000
+ 3D 3 2 0.00 1.700 1.700 0.100000
+ Pseudization used: troullier-martins
+
+
+ &input
+ title='Si',
+ zed=14.,
+ rel=1,
+ config='[Ne] 3s2 3p2 3d-1',
+ iswitch=3,
+ dft='PBE'
+ /
+ &inputp
+ lpaw=.false.,
+ use_xsd=.FALSE.,
+ pseudotype=3,
+ file_pseudopw='Si.pbe-nl-rrkjus_psl.1.0.0.UPF',
+ author='ADC',
+ lloc=2,
+ which_augfun='PSQ',
+ rmatch_augfun_nc=.true.,
+ nlcc=.true.,
+ new_core_ps=.true.,
+ rcore=1.4,
+ tm=.true.
+ /
+5
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+3S 1 0 0.00 6.00 1.60 1.80 0.0
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+3P 2 1 0.00 6.00 1.60 1.80 0.0
+3D 3 2 0.00 0.10 1.70 1.70 0.0
+
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+ 1.150227163249662e-9 2.001761235456282e-9 2.593277737779580e-9 2.985129829299777e-9
+ 3.225039891821955e-9 3.350409516677662e-9 3.390531769607724e-9 3.368266603357291e-9
+ 3.301201815648151e-9 3.202924950831607e-9 3.368622490174865e-10 4.194074442648387e-10
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+ 6.507467079344066e-10 6.506975975672345e-10 6.506513547481313e-10 6.506223680326184e-10
+ 6.505827759449239e-10 6.505564425547554e-10 6.505121503211290e-10 6.504604390158724e-10
+ 6.504399751425596e-10 6.504011813990629e-10 6.503819675792702e-10 6.503362006882016e-10
+ 6.502852809695327e-10 6.502690525144250e-10 6.502225401859857e-10 6.502034035732132e-10
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+ 6.500436369640537e-10 6.500005987297869e-10 6.499642040143270e-10 6.499541741522772e-10
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+ 6.498126786287861e-10 6.497731681976977e-10 6.497702212072554e-10 6.497329537340675e-10
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+ 6.495437468368925e-10 6.495170077051455e-10 6.494865967803507e-10 6.494876796673797e-10
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+ 6.494104101586979e-10 6.493837249890033e-10 6.493882684584664e-10 6.493664049053395e-10
+ 6.493434304367512e-10 6.493448220181773e-10 6.493293337502461e-10 6.493382498439530e-10
+ 6.493166510925301e-10 6.492993891110926e-10 6.493125362922354e-10 6.492932850674670e-10
+ 6.492969626363738e-10 6.492795086223567e-10 6.492709792634049e-10 6.492804955728920e-10
+ 6.492696192308456e-10 6.492810710436423e-10 6.492651926352462e-10 6.492615767312049e-10
+ 6.492719857522181e-10 6.492646317932148e-10 6.492836949407431e-10 6.492694363586549e-10
+ 6.492763184148583e-10
+
+
+
+
diff --git a/requirements.txt b/requirements.txt
index ddabb08..75565ff 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
-jax>=0.4.14
-jaxlib>=0.4.14
+jax>=0.5.3
+jaxlib>=0.5.3
optax>=0.1.4
jaxtyping
@@ -8,3 +8,6 @@ ase
absl-py
einops
ml_collections
+
+jax_xc==0.0.8
+cloudpickle
diff --git a/setup.cfg b/setup.cfg
index 0ac19e4..fa1735a 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -12,6 +12,7 @@ exclude =
indent_size = 2
max-line-length = 80
extend-ignore =
+ E741
E731
E124
W605
diff --git a/setup.py b/setup.py
index c1328d1..73d68fd 100644
--- a/setup.py
+++ b/setup.py
@@ -41,7 +41,7 @@ def _read_requirements():
install_requires=_read_requirements(),
entry_points={
'console_scripts': [
- 'jrystal=main',
+ 'jrystal=main:main',
],
},
)
diff --git a/tests/all_electron/al/config.yaml b/tests/all_electron/al/config.yaml
new file mode 100644
index 0000000..a4f61e1
--- /dev/null
+++ b/tests/all_electron/al/config.yaml
@@ -0,0 +1,124 @@
+################################################
+# Crystal Setting #
+################################################
+
+# Code will load the "$CRYSTAL.xyz" file in the geometry directory if
+# crystal name is provided, otherwise the code will read the from the path
+# specified by "crystal_file_path_path".
+crystal: "al_conventional"
+crystal_file_path_path: null
+
+################################################
+# Exchange-Correlation Setting #
+################################################
+# xc functional
+xc: "lda_x"
+
+################################################
+# Pseudopotential Setting #
+################################################
+
+use_pseudopotential: False
+pseudopotential_file_dir: null
+# The pseudopotential file directory. If None, the code will use the default
+# pseudopotential file directory.
+
+################################################
+# Planewave Hyper-parameters #
+################################################
+
+freq_mask_method: "spherical"
+# freq_mask_method can be either "cubic" or "spherical".
+# For "cubic", the cutoff cannot be assigned, but will be estimated.
+# Only for "spherical" method, the cutoff_energy argument is valid.
+
+# cutoff energy in Hartree
+cutoff_energy: 100
+# grid sizes for the FFT grid
+grid_sizes: 72
+# k grid sizes for the Brillouin zone sampling using Monkhorst-Pack scheme
+k_grid_sizes: 3
+# Occupation method
+occupation: "fermi-dirac"
+# occupation support {"fermi-dirac", "gamma", "uniform"}
+
+# smearing factor for the Fermi-Dirac distribution
+smearing: 0.001
+# the total number of unoccupapied bands. The Total number of
+# potentially-occupied bands is ceil(num_electron / 2) + empty_bands
+empty_bands: 14
+spin_restricted: True
+# whether the calculation is spin_restricted or not, ie., spin-up and spin-down
+# electrons share the same spatial orbitals.
+
+# Hyperparameters for the Ewald sum.
+ewald_args:
+ ewald_eta: 0.1
+ ewald_cutoff: 2.0e+4
+
+################################################
+# Optimizizer Setting #
+################################################
+epoch: 10000
+optimizer: "adam"
+optimizer_args:
+ learning_rate: 0.001
+
+# arguments for the optimizer. Please refer to optax documentation.
+# https://optax.readthedocs.io/en/latest/api/optimizers.html
+# WARNING: the learning_rate is sensitve to the number of G vectors and
+# cutoff energy. It is suggested to tune the learning rate for different
+# systems.
+
+# "scheduler": "piecewise_constant_schedule",
+scheduler: null
+# optax scheduler. No scheduler is used if set to None.
+
+convergence_condition: 1.e-8
+# Convergence criterion for optimization.
+# The code monitors the variance of the objective values (e.g., energy)
+# over the last 50 steps (if available). If the computed variance meets
+# the convergence condition, the calculation will terminate.
+
+################################################
+# Band Structure Calculation Hyper-parameters. #
+################################################
+
+# NOTE: The following configs are only valid for band structure calculation.
+# Need to either indicate the k_path or the k_path file.
+band_structure_empty_bands: null
+k_path_special_points: null
+# If `k_path_special_points` is None, the code will use the default
+# special points for the given unit cell, as defined in ASE.
+# For more details, refer to:
+# https://wiki.fysik.dtu.dk/ase/ase/dft/kpoints.html
+
+k_path_special_points: "GXMG"
+num_kpoints: 60
+
+# Alternatively, the user can provide a `k_path` file containing k-path
+# vectors. The file must be in `.npy` format and contain a 2D array,
+# where each row represents the relative coordinates of a k-point in
+# reciprocal space.
+k_path_file: null
+band_structure_epoch: 10000
+
+# Flag indicating whether fine-tuning is enabled.
+# If True, the optimization will start with the first k-point,
+# using its parameters as the initialization for subsequent k-points.
+# This approach is recommended for large crystal systems.
+k_path_fine_tuning: True
+k_path_fine_tuning_epoch: 400
+
+################################################
+# System Setting #
+################################################
+
+# Random seed
+seed: 123
+
+# Environment setting
+xla_preallocate: True
+jax_enable_x64: True
+verbose: True
+eps: 1.e-08
diff --git a/tests/all_electron/al/main.sh b/tests/all_electron/al/main.sh
new file mode 100644
index 0000000..2af62ad
--- /dev/null
+++ b/tests/all_electron/al/main.sh
@@ -0,0 +1,4 @@
+#! /bin/bash
+
+jrystal -m band -c config.yaml
+python plot.py
diff --git a/tests/all_electron/al/plot.py b/tests/all_electron/al/plot.py
new file mode 100644
index 0000000..6972386
--- /dev/null
+++ b/tests/all_electron/al/plot.py
@@ -0,0 +1,44 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+jr_eigen = np.load("AlAlAlAl_band_structure.npy")*27.2114
+qe_eigen = np.load("qe.npy").T
+
+
+### comparing jr and qe
+
+plt.figure(figsize=(4, 3.6))
+
+fermi = jr_eigen[0, 25]
+plt.plot(jr_eigen - fermi, color="blue", alpha = 0.3, lw=2)
+
+
+fermi = 0
+for i in range(qe_eigen.shape[1]):
+ plt.scatter(range(60), qe_eigen[:, i]-fermi, color="brown", marker="v", alpha=1, s=3)
+
+plt.xticks([0, 17, 34, 60], ["$\Gamma$", "X", "M", "$\Gamma$"])
+plt.vlines(17, 100, -100, linestyles="dotted", color="grey")
+plt.vlines(34, 100, -100, linestyles="dotted", color="grey")
+plt.hlines(0, 0, 60, linestyles="dotted", color="grey")
+plt.yticks([-10, -5, 0, 5, 10])
+plt.ylabel("Eigenvalue (eV)")
+plt.xlabel("K point")
+plt.title("Al")
+
+custom_handles = [
+ Line2D([0], [0], color='blue', label="Our method"),
+ Line2D([0], [0], marker='v', color='w', markerfacecolor='brown', alpha=1, markersize=6, label="Quantum Espresso")
+]
+
+# Custom legend
+plt.legend(handles=custom_handles, loc='lower right')
+
+
+plt.ylim(-10, 10)
+plt.xlim(0, 60)
+plt.tight_layout()
+plt.savefig("overlay.pdf")
+plt.show()
+
diff --git a/tests/all_electron/al/qe.npy b/tests/all_electron/al/qe.npy
new file mode 100644
index 0000000..01faea6
Binary files /dev/null and b/tests/all_electron/al/qe.npy differ
diff --git a/tests/all_electron/si/config.yaml b/tests/all_electron/si/config.yaml
new file mode 100644
index 0000000..24b0434
--- /dev/null
+++ b/tests/all_electron/si/config.yaml
@@ -0,0 +1,131 @@
+################################################
+# Crystal Setting #
+################################################
+
+# Code will load the "$CRYSTAL.xyz" file in the geometry directory if
+# crystal name is provided, otherwise the code will read the from the path
+# specified by "crystal_file_path_path".
+crystal: "si"
+crystal_file_path_path: null
+
+################################################
+# Exchange-Correlation Setting #
+################################################
+# xc functional
+xc: "lda_x"
+# xc: "pbe"
+
+################################################
+# Pseudopotential Setting #
+################################################
+
+use_pseudopotential: False
+pseudopotential_file_dir: null
+# The pseudopotential file directory. If None, the code will use the default
+# pseudopotential file directory.
+
+################################################
+# Planewave Hyper-parameters #
+################################################
+
+freq_mask_method: "spherical"
+# freq_mask_method can be either "cubic" or "spherical".
+# For "cubic", the cutoff cannot be assigned, but will be estimated.
+# Only for "spherical" method, the cutoff_energy argument is valid.
+
+# cutoff energy in Hartree
+cutoff_energy: 100
+# grid sizes for the FFT grid
+grid_sizes: 64
+# k grid sizes for the Brillouin zone sampling using Monkhorst-Pack scheme
+k_grid_sizes: 3
+# Occupation method
+occupation: "fermi-dirac"
+# occupation support {"fermi-dirac", "gamma", "uniform"}
+
+# smearing factor for the Fermi-Dirac distribution
+smearing: 0.01
+# the total number of unoccupapied bands. The Total number of
+# potentially-occupied bands is ceil(num_electron / 2) + empty_bands
+empty_bands: 14
+spin_restricted: True
+# whether the calculation is spin_restricted or not, ie., spin-up and spin-down
+# electrons share the same spatial orbitals.
+
+# Hyperparameters for the Ewald sum.
+ewald_args:
+ ewald_eta: 0.1
+ ewald_cutoff: 2.0e+4
+
+################################################
+# Optimizizer Setting #
+################################################
+epoch: 10000
+optimizer: "adam"
+optimizer_args:
+ learning_rate: 0.001
+
+# arguments for the optimizer. Please refer to optax documentation.
+# https://optax.readthedocs.io/en/latest/api/optimizers.html
+# WARNING: the learning_rate is sensitve to the number of G vectors and
+# cutoff energy. It is suggested to tune the learning rate for different
+# systems.
+
+# "scheduler": "piecewise_constant_schedule",
+scheduler: null
+# optax scheduler. No scheduler is used if set to None.
+
+convergence_condition: 1.e-8
+# Convergence criterion for optimization.
+# The code monitors the variance of the objective values (e.g., energy)
+# over the last 50 steps (if available). If the computed variance meets
+# the convergence condition, the calculation will terminate.
+
+################################################
+# Band Structure Calculation Hyper-parameters. #
+################################################
+
+# NOTE: The following configs are only valid for band structure calculation.
+# Need to either indicate the k_path or the k_path file.
+band_structure_empty_bands: null
+k_path_special_points: null
+# If `k_path_special_points` is None, the code will use the default
+# special points for the given unit cell, as defined in ASE.
+# For more details, refer to:
+# https://wiki.fysik.dtu.dk/ase/ase/dft/kpoints.html
+
+"k_path_special_points": "LGXL"
+num_kpoints: 60
+
+# Alternatively, the user can provide a `k_path` file containing k-path
+# vectors. The file must be in `.npy` format and contain a 2D array,
+# where each row represents the relative coordinates of a k-point in
+# reciprocal space.
+k_path_file: null
+band_structure_epoch: 10000
+
+# Flag indicating whether fine-tuning is enabled.
+# If True, the optimization will start with the first k-point,
+# using its parameters as the initialization for subsequent k-points.
+# This approach is recommended for large crystal systems.
+k_path_fine_tuning: True
+k_path_fine_tuning_epoch: 400
+
+################################################
+# System Setting #
+################################################
+
+# Random seed
+seed: 123
+
+# Environment setting
+xla_preallocate: True
+jax_enable_x64: True
+verbose: True
+eps: 1.e-08
+
+debug: False
+
+jax_debug_nans: False
+spin: 0
+convergence_window_size: 50
diff --git a/tests/all_electron/si/main.sh b/tests/all_electron/si/main.sh
new file mode 100755
index 0000000..343c0d0
--- /dev/null
+++ b/tests/all_electron/si/main.sh
@@ -0,0 +1,8 @@
+#! /bin/bash
+
+jrystal -m band -c config.yaml
+python plot.py
+
+
+
+
diff --git a/tests/all_electron/si/plot.py b/tests/all_electron/si/plot.py
new file mode 100644
index 0000000..7089533
--- /dev/null
+++ b/tests/all_electron/si/plot.py
@@ -0,0 +1,43 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+jr_eigen = np.load("SiSi_band_structure.npy")*27.2114
+jr_eigen = jr_eigen[:, 0] # remove spin channel
+
+qe_eigen = np.load("qe.npy").T
+
+
+
+### comparing jr and qe
+
+plt.figure(figsize=(4, 3.6))
+
+fermi = np.max(jr_eigen, axis=0)[13]
+plt.plot(jr_eigen - fermi, color="blue", alpha = 0.3, lw=2)
+
+fermi = np.max(qe_eigen, axis=0)[13]
+for i in range(qe_eigen.shape[1]):
+ plt.scatter(range(60), qe_eigen[:, i]-fermi, color="brown", marker="v", alpha=1, s=3)
+
+plt.vlines(18, 100, -100, linestyles="dotted", color="grey")
+plt.vlines(40, 100, -100, linestyles="dotted", color="grey")
+plt.hlines(0, 0, 60, linestyles="dotted", color="grey")
+plt.xticks([0, 18, 40, 60], ["L", "$\Gamma$", "X", "L"])
+plt.yticks([-20, -10, 0, 10, 20])
+plt.ylabel("Eigenvalue (eV)")
+plt.xlabel("K point")
+plt.title("Si")
+
+custom_handles = [
+ Line2D([0], [0], color='blue', label="Our method"),
+ Line2D([0], [0], marker='v', color='w', markerfacecolor='brown', alpha=1, markersize=6, label="Quantum espresso")
+]
+
+# Custom legend
+plt.legend(handles=custom_handles, loc='lower right')
+
+plt.ylim(-10, 10)
+plt.xlim(0, 60)
+plt.tight_layout()
+plt.savefig("overlay.pdf")
diff --git a/tests/all_electron/si/qe.npy b/tests/all_electron/si/qe.npy
new file mode 100644
index 0000000..2fab734
Binary files /dev/null and b/tests/all_electron/si/qe.npy differ
diff --git a/tests/normconserving/al/config.yaml b/tests/normconserving/al/config.yaml
new file mode 100644
index 0000000..7f5ce1b
--- /dev/null
+++ b/tests/normconserving/al/config.yaml
@@ -0,0 +1,124 @@
+################################################
+# Crystal Setting #
+################################################
+
+# Code will load the "$CRYSTAL.xyz" file in the geometry directory if
+# crystal name is provided, otherwise the code will read the from the path
+# specified by "crystal_file_path_path".
+crystal: "al_bcc"
+crystal_file_path_path: null
+
+################################################
+# Exchange-Correlation Setting #
+################################################
+# xc functional
+xc: "lda_x"
+
+################################################
+# Pseudopotential Setting #
+################################################
+
+use_pseudopotential: True
+pseudopotential_file_dir: null
+# The pseudopotential file directory. If None, the code will use the default
+# pseudopotential file directory.
+
+################################################
+# Planewave Hyper-parameters #
+################################################
+
+freq_mask_method: "spherical"
+# freq_mask_method can be either "cubic" or "spherical".
+# For "cubic", the cutoff cannot be assigned, but will be estimated.
+# Only for "spherical" method, the cutoff_energy argument is valid.
+
+# cutoff energy in Hartree
+cutoff_energy: 50
+# grid sizes for the FFT grid
+grid_sizes: 48
+# k grid sizes for the Brillouin zone sampling using Monkhorst-Pack scheme
+k_grid_sizes: 1
+# Occupation method
+occupation: "fermi-dirac"
+# occupation support {"fermi-dirac", "gamma", "uniform"}
+
+# smearing factor for the Fermi-Dirac distribution
+smearing: 0.01
+# the total number of unoccupapied bands. The Total number of
+# potentially-occupied bands is ceil(num_electron / 2) + empty_bands
+empty_bands: 14
+spin_restricted: True
+# whether the calculation is spin_restricted or not, ie., spin-up and spin-down
+# electrons share the same spatial orbitals.
+
+# Hyperparameters for the Ewald sum.
+ewald_args:
+ ewald_eta: 0.1
+ ewald_cutoff: 2.0e+4
+
+################################################
+# Optimizizer Setting #
+################################################
+epoch: 10000
+optimizer: "adam"
+optimizer_args:
+ learning_rate: 0.001
+
+# arguments for the optimizer. Please refer to optax documentation.
+# https://optax.readthedocs.io/en/latest/api/optimizers.html
+# WARNING: the learning_rate is sensitve to the number of G vectors and
+# cutoff energy. It is suggested to tune the learning rate for different
+# systems.
+
+# "scheduler": "piecewise_constant_schedule",
+scheduler: null
+# optax scheduler. No scheduler is used if set to None.
+
+convergence_condition: 1.e-8
+# Convergence criterion for optimization.
+# The code monitors the variance of the objective values (e.g., energy)
+# over the last 50 steps (if available). If the computed variance meets
+# the convergence condition, the calculation will terminate.
+
+################################################
+# Band Structure Calculation Hyper-parameters. #
+################################################
+
+# NOTE: The following configs are only valid for band structure calculation.
+# Need to either indicate the k_path or the k_path file.
+band_structure_empty_bands: null
+k_path_special_points: null
+# If `k_path_special_points` is None, the code will use the default
+# special points for the given unit cell, as defined in ASE.
+# For more details, refer to:
+# https://wiki.fysik.dtu.dk/ase/ase/dft/kpoints.html
+
+k_path_special_points: "LGXL"
+num_kpoints: 60
+
+# Alternatively, the user can provide a `k_path` file containing k-path
+# vectors. The file must be in `.npy` format and contain a 2D array,
+# where each row represents the relative coordinates of a k-point in
+# reciprocal space.
+k_path_file: null
+band_structure_epoch: 10000
+
+# Flag indicating whether fine-tuning is enabled.
+# If True, the optimization will start with the first k-point,
+# using its parameters as the initialization for subsequent k-points.
+# This approach is recommended for large crystal systems.
+k_path_fine_tuning: True
+k_path_fine_tuning_epoch: 400
+
+################################################
+# System Setting #
+################################################
+
+# Random seed
+seed: 123
+
+# Environment setting
+xla_preallocate: True
+jax_enable_x64: True
+verbose: True
+eps: 1.e-08
diff --git a/tests/normconserving/al/main.sh b/tests/normconserving/al/main.sh
new file mode 100644
index 0000000..2af62ad
--- /dev/null
+++ b/tests/normconserving/al/main.sh
@@ -0,0 +1,4 @@
+#! /bin/bash
+
+jrystal -m band -c config.yaml
+python plot.py
diff --git a/tests/normconserving/al/plot.py b/tests/normconserving/al/plot.py
new file mode 100644
index 0000000..7e855d4
--- /dev/null
+++ b/tests/normconserving/al/plot.py
@@ -0,0 +1,42 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+jr_eigen = np.load("AlAl_band_structure.npy")*27.2114
+qe_eigen = np.load("qe.npy").T
+
+
+### comparing jr and qe
+
+plt.figure(figsize=(4, 3.6))
+fermi = jr_eigen[18, 3]
+plt.plot(jr_eigen - fermi, color="blue", alpha = 0.3, lw=2)
+
+# fermi = qe_eigen[18, 3]
+fermi = 0
+for i in range(qe_eigen.shape[1]):
+ plt.scatter(range(60), qe_eigen[:, i]-fermi, color="brown", marker="v", alpha=1, s=3)
+
+plt.vlines(18, 100, -100, linestyles="dotted", color="grey")
+plt.vlines(40, 100, -100, linestyles="dotted", color="grey")
+plt.hlines(0, 0, 60, linestyles="dotted", color="grey")
+plt.xticks([0, 18, 40, 60], ["L", "$\Gamma$", "X", "L"])
+plt.yticks([-20, -10, 0, 10, 20])
+plt.ylabel("Eigenvalue (eV)")
+plt.xlabel("K point")
+plt.title("Al")
+
+custom_handles = [
+ Line2D([0], [0], color='blue', label="Our method"),
+ Line2D([0], [0], marker='v', color='w', markerfacecolor='brown', alpha=1, markersize=6, label="Quantum Espresso")
+]
+
+# Custom legend
+plt.legend(handles=custom_handles, loc='lower right')
+
+
+plt.ylim(-25, 15)
+plt.xlim(0, 60)
+plt.tight_layout()
+plt.savefig("overlay.pdf")
+plt.show()
diff --git a/tests/normconserving/al/qe.npy b/tests/normconserving/al/qe.npy
new file mode 100644
index 0000000..357fbd6
Binary files /dev/null and b/tests/normconserving/al/qe.npy differ
diff --git a/tests/normconserving/diamond/config.yaml b/tests/normconserving/diamond/config.yaml
new file mode 100644
index 0000000..5640141
--- /dev/null
+++ b/tests/normconserving/diamond/config.yaml
@@ -0,0 +1,124 @@
+################################################
+# Crystal Setting #
+################################################
+
+# Code will load the "$CRYSTAL.xyz" file in the geometry directory if
+# crystal name is provided, otherwise the code will read the from the path
+# specified by "crystal_file_path_path".
+crystal: "diamond"
+crystal_file_path_path: null
+
+################################################
+# Exchange-Correlation Setting #
+################################################
+# xc functional
+xc: "lda_x"
+
+################################################
+# Pseudopotential Setting #
+################################################
+
+use_pseudopotential: True
+pseudopotential_file_dir: null
+# The pseudopotential file directory. If None, the code will use the default
+# pseudopotential file directory.
+
+################################################
+# Planewave Hyper-parameters #
+################################################
+
+freq_mask_method: "spherical"
+# freq_mask_method can be either "cubic" or "spherical".
+# For "cubic", the cutoff cannot be assigned, but will be estimated.
+# Only for "spherical" method, the cutoff_energy argument is valid.
+
+# cutoff energy in Hartree
+cutoff_energy: 100
+# grid sizes for the FFT grid
+grid_sizes: 48
+# k grid sizes for the Brillouin zone sampling using Monkhorst-Pack scheme
+k_grid_sizes: 2
+# Occupation method
+occupation: "fermi-dirac"
+# occupation support {"fermi-dirac", "gamma", "uniform"}
+
+# smearing factor for the Fermi-Dirac distribution
+smearing: 0.001
+# the total number of unoccupapied bands. The Total number of
+# potentially-occupied bands is ceil(num_electron / 2) + empty_bands
+empty_bands: 10
+spin_restricted: True
+# whether the calculation is spin_restricted or not, ie., spin-up and spin-down
+# electrons share the same spatial orbitals.
+
+# Hyperparameters for the Ewald sum.
+ewald_args:
+ ewald_eta: 0.1
+ ewald_cutoff: 2.0e+4
+
+################################################
+# Optimizizer Setting #
+################################################
+epoch: 10000
+optimizer: "adam"
+optimizer_args:
+ learning_rate: 0.001
+
+# arguments for the optimizer. Please refer to optax documentation.
+# https://optax.readthedocs.io/en/latest/api/optimizers.html
+# WARNING: the learning_rate is sensitve to the number of G vectors and
+# cutoff energy. It is suggested to tune the learning rate for different
+# systems.
+
+# "scheduler": "piecewise_constant_schedule",
+scheduler: null
+# optax scheduler. No scheduler is used if set to None.
+
+convergence_condition: 1.e-8
+# Convergence criterion for optimization.
+# The code monitors the variance of the objective values (e.g., energy)
+# over the last 50 steps (if available). If the computed variance meets
+# the convergence condition, the calculation will terminate.
+
+################################################
+# Band Structure Calculation Hyper-parameters. #
+################################################
+
+# NOTE: The following configs are only valid for band structure calculation.
+# Need to either indicate the k_path or the k_path file.
+band_structure_empty_bands: null
+k_path_special_points: null
+# If `k_path_special_points` is None, the code will use the default
+# special points for the given unit cell, as defined in ASE.
+# For more details, refer to:
+# https://wiki.fysik.dtu.dk/ase/ase/dft/kpoints.html
+
+k_path_special_points: "LGXL"
+num_kpoints: 60
+
+# Alternatively, the user can provide a `k_path` file containing k-path
+# vectors. The file must be in `.npy` format and contain a 2D array,
+# where each row represents the relative coordinates of a k-point in
+# reciprocal space.
+k_path_file: null
+band_structure_epoch: 10000
+
+# Flag indicating whether fine-tuning is enabled.
+# If True, the optimization will start with the first k-point,
+# using its parameters as the initialization for subsequent k-points.
+# This approach is recommended for large crystal systems.
+k_path_fine_tuning: True
+k_path_fine_tuning_epoch: 400
+
+################################################
+# System Setting #
+################################################
+
+# Random seed
+seed: 123
+
+# Environment setting
+xla_preallocate: True
+jax_enable_x64: True
+verbose: True
+eps: 1.e-08
diff --git a/tests/normconserving/diamond/main.sh b/tests/normconserving/diamond/main.sh
new file mode 100644
index 0000000..2af62ad
--- /dev/null
+++ b/tests/normconserving/diamond/main.sh
@@ -0,0 +1,4 @@
+#! /bin/bash
+
+jrystal -m band -c config.yaml
+python plot.py
diff --git a/tests/normconserving/diamond/plot.py b/tests/normconserving/diamond/plot.py
new file mode 100644
index 0000000..88ab167
--- /dev/null
+++ b/tests/normconserving/diamond/plot.py
@@ -0,0 +1,43 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+jr_eigen = np.load("CC_band_structure.npy")*27.2114
+qe_eigen = np.load("qe.npy").T
+
+
+### comparing jr and qe
+
+plt.figure(figsize=(4, 3.6))
+
+fermi = jr_eigen[18, 3]
+plt.plot(jr_eigen - fermi, color="blue", alpha = 0.3, lw=2)
+
+# fermi = qe_eigen[18, 3]
+fermi = 0
+for i in range(qe_eigen.shape[1]):
+ plt.scatter(range(60), qe_eigen[:, i]-fermi, color="brown", marker="v", alpha=1, s=3)
+
+plt.vlines(18, 100, -100, linestyles="dotted", color="grey")
+plt.vlines(40, 100, -100, linestyles="dotted", color="grey")
+plt.hlines(0, 0, 60, linestyles="dotted", color="grey")
+plt.xticks([0, 18, 40, 60], ["L", "$\Gamma$", "X", "L"])
+plt.yticks([-20, -10, 0, 10, 20])
+plt.ylabel("Eigenvalue (eV)")
+plt.xlabel("K point")
+plt.title("Carbon (Diamond)")
+
+custom_handles = [
+ Line2D([0], [0], color='blue', label="Our method"),
+ Line2D([0], [0], marker='v', color='w', markerfacecolor='brown', alpha=1, markersize=6, label="Quantum Espresso")
+]
+
+# Custom legend
+plt.legend(handles=custom_handles, loc='lower right')
+
+
+plt.ylim(-25, 25)
+plt.xlim(0, 60)
+plt.tight_layout()
+plt.savefig("overlay.pdf")
+plt.show()
diff --git a/tests/normconserving/diamond/qe.npy b/tests/normconserving/diamond/qe.npy
new file mode 100644
index 0000000..e9816c4
Binary files /dev/null and b/tests/normconserving/diamond/qe.npy differ
+ 
-**Differentiable:** Leveraging JAX's automatic differentiation for efficient gradient computation of quantum properties, enabling straightforward optimization workflows.
-
-
-**GPU-Accelerated:** Optimized for modern GPU architectures, delivering high-performance quantum calculations with automatic hardware acceleration.
-
-
-**Solid-State Calculation:** Full-featured framework for periodic systems using plane wave basis sets, supporting precise electronic structure calculations of crystalline materials.
-
-
-**Direct Optimization:** A direct minimization approach that avoids SCF iterations, enabling smooth convergence and natural integration of machine learning methods, and advanced quantum chemistry methods into density functional theory calculations.
+**Direct Optimization:** A direct minimization approach that avoids SCF iterations, enabling smooth convergence and natural integration of machine learning methods, and advanced quantum chemistry methods into density functional theory calculations. 
+ 
+ 
+ 
+