Torch Autodiff Multicharge

Compatibility:
Availability:
Status:

PyTorch implementation of the electronegativity equilibration (EEQ) model for atomic partial charges. This module allows to process a single structure or a batch of structures for the calculation of atom-resolved dispersion energies.

If you use this software, please cite the following publication

• M. Friede, C. Hölzer, S. Ehlert, S. Grimme, J. Chem. Phys., 2024, 161, 062501. DOI: 10.1063/5.0216715

For details on the EEQ model, see

• S. A. Ghasemi, A. Hofstetter, S. Saha, and S. Goedecker, Phys. Rev. B, 2015, 92, 045131. DOI: 10.1103/PhysRevB.92.045131

• E. Caldeweyher, S. Ehlert, A. Hansen, H. Neugebauer, S. Spicher, C. Bannwarth and S. Grimme, J. Chem. Phys., 2019, 150, 154122. DOI: 10.1063/1.5090222

For alternative implementations, also check out

• multicharge: Implementation of the EEQ model in Fortran.

Installation

pip

tad-multicharge can easily be installed with pip.

pip install tad-multicharge

conda

tad-multicharge is also available from conda.

conda install tad-multicharge

From source

This project is hosted on GitHub at tad-mctc/tad-multicharge. Obtain the source by cloning the repository with

git clone https://github.com/tad-mctc/tad-multicharge
cd tad-multicharge

We recommend using a conda environment to install the package. You can setup the environment manager using a mambaforge installer. Install the required dependencies from the conda-forge channel.

mamba env create -n torch -f environment.yaml
mamba activate torch

Install this project with pip in the environment

pip install .

The following dependencies are required

• numpy
• tad-mctc
• pytest (tests only)
• torch

Compatibility

✅ tested in CI    ✔️ supported, but not tested in CI (should still work)    ❌ not supported

Note that only the latest bug fix version is listed, but all preceding bug fix minor versions are supported. For example, although only version 2.7.1 is listed, version 2.7.0 is also supported.

Development

For development, additionally install the following tools in your environment.

mamba install black covdefaults mypy pre-commit pylint pytest pytest-cov pytest-xdist tox
pip install pytest-random-order

With pip, add the option -e for installing in development mode, and add [dev] for the development dependencies

pip install -e .[dev]

The pre-commit hooks are initialized by running the following command in the root of the repository.

pre-commit install

For testing all Python environments, simply run tox.

tox

Note that this randomizes the order of tests but skips "large" tests. To modify this behavior, tox has to skip the optional posargs.

tox -- test

Examples

The following example shows how to calculate the EEQ partial charges and the corresponding electrostatic energy for a single structure.

import torch
from tad_mctc.io.structure import Structure
from tad_multicharge import eeq

numbers = torch.tensor([7, 7, 1, 1, 1, 1, 1, 1])

# coordinates in Bohr
positions = torch.tensor(
    [
        [-2.98334550857544, -0.08808205276728, +0.00000000000000],
        [+2.98334550857544, +0.08808205276728, +0.00000000000000],
        [-4.07920360565186, +0.25775116682053, +1.52985656261444],
        [-1.60526800155640, +1.24380481243134, +0.00000000000000],
        [-4.07920360565186, +0.25775116682053, -1.52985656261444],
        [+4.07920360565186, -0.25775116682053, -1.52985656261444],
        [+1.60526800155640, -1.24380481243134, +0.00000000000000],
        [+4.07920360565186, -0.25775116682053, +1.52985656261444],
    ]
)

# an absent `charge` means neutral
structure = Structure(numbers=numbers, positions=positions)
cn = torch.tensor([3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])

eeq_model = eeq.EEQModel.param2019()
qat, energy = eeq_model.solve(structure, cn, return_energy=True)

print(torch.sum(energy, -1))
# tensor(-0.1750)
print(qat)
# tensor([-0.8347, -0.8347,  0.2731,  0.2886,  0.2731,  0.2731,  0.2886,  0.2731])

The next example shows the calculation of the electrostatic energy with a simpler API for a batch of structures. The coordination number is computed internally with tad_mctc.ncoord.cn_eeq.

import torch
from tad_mctc.batch import pack
from tad_mctc.convert import symbol_to_number
from tad_mctc.io.structure import Structure
from tad_multicharge import eeq

# S22 system 4: formamide dimer
numbers = pack(
    (
        symbol_to_number("C C N N H H H H H H O O".split()),
        symbol_to_number("C O N H H H".split()),
    )
)

# coordinates in Bohr
positions = pack(
    (
        torch.tensor(
            [
                [-3.81469488143921, +0.09993441402912, 0.00000000000000],
                [+3.81469488143921, -0.09993441402912, 0.00000000000000],
                [-2.66030049324036, -2.15898251533508, 0.00000000000000],
                [+2.66030049324036, +2.15898251533508, 0.00000000000000],
                [-0.73178529739380, -2.28237795829773, 0.00000000000000],
                [-5.89039325714111, -0.02589114569128, 0.00000000000000],
                [-3.71254944801331, -3.73605775833130, 0.00000000000000],
                [+3.71254944801331, +3.73605775833130, 0.00000000000000],
                [+0.73178529739380, +2.28237795829773, 0.00000000000000],
                [+5.89039325714111, +0.02589114569128, 0.00000000000000],
                [-2.74426102638245, +2.16115570068359, 0.00000000000000],
                [+2.74426102638245, -2.16115570068359, 0.00000000000000],
            ]
        ),
        torch.tensor(
            [
                [-0.55569743203406, +1.09030425468557, 0.00000000000000],
                [+0.51473634678469, +3.15152550263611, 0.00000000000000],
                [+0.59869690244446, -1.16861263789477, 0.00000000000000],
                [-0.45355203669134, -2.74568780438064, 0.00000000000000],
                [+2.52721209544999, -1.29200800956867, 0.00000000000000],
                [-2.63139587595376, +0.96447869452240, 0.00000000000000],
            ]
        ),
    )
)

# total charge of both systems
charge = torch.tensor([0.0, 0.0])
structure = Structure(numbers=numbers, positions=positions, charge=charge)

# calculate electrostatic energy in Hartree
energy = torch.sum(eeq.get_energy(structure), -1)

print(energy)
# tensor([-0.2087, -0.0972])
print(energy[0] - 2 * energy[1])
# tensor(-0.0143)

The model is a frozen Node (a pytree), so it works with torch.func.vmap, jacrev, jacfwd and torch.compile(fullgraph=True). For example, the derivative of the energy with respect to all parameters is a single jacrev over the model, and a different coordination number is set with replace.

import torch
from tad_mctc.ncoord import cn_eeq
from tad_multicharge import eeq

model = eeq.EEQModel.param2019().replace(cn=cn_eeq.replace(cutoff=20.0))

grad = torch.func.jacrev(
    lambda m: m(structure, return_energy=True)[1].sum()
)(model)
print(grad.chi.shape)
# torch.Size([104])

Contributing

This is a volunteer open source projects and contributions are always welcome. Please, take a moment to read the contributing guidelines.

License

This project is licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files 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.