Torch Autodiff implementation of charge models
Project description
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.
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 <https://github.com/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
Compatibility
PyTorch \ Python | 3.8 | 3.9 | 3.10 | 3.11 | 3.12 |
---|---|---|---|---|---|
1.11.0 | :white_check_mark: | :white_check_mark: | :x: | :x: | :x: |
1.12.1 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: | :x: |
1.13.1 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
2.0.1 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
2.1.2 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :x: |
2.2.2 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
2.3.1 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
2.4.1 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
Note that only the latest bug fix version is listed, but all preceding bug fix minor versions are supported. For example, although only version 2.2.2 is listed, version 2.2.0 and 2.2.1 are also supported.
On macOS and Windows, PyTorch<2.0.0 does only support Python<3.11.
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_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],
]
)
total_charge = torch.tensor(0.0)
cn = torch.tensor([3.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
eeq_model = eeq.EEQModel.param2019()
energy, qat = eeq_model.solve(numbers, positions, total_charge, cn)
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.
import torch
from tad_multicharge import eeq
from tad_mctc.batch import pack
from tad_mctc.convert import symbol_to_number
# 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 system
charge = torch.tensor([0.0, 0.0])
# calculate electrostatic energy in Hartree
energy = torch.sum(eeq.get_energy(numbers, positions, charge), -1)
torch.set_printoptions(precision=10)
print(energy)
# tensor([-0.2086755037, -0.0972094536])
print(energy[0] - 2 * energy[1])
# tensor(-0.0142565966)
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.
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