tad-dftd3 0.1.4

Torch autodiff DFT-D3 implementation

Implementation of the DFT-D3 dispersion model in PyTorch. 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 D3 dispersion model see

• J. Chem. Phys., 2010, 132, 154104 (DOI)

• J. Comput. Chem., 2011, 32, 1456 (DOI)

For alternative implementations also check out

simple-dftd3:

Simple reimplementation of the DFT-D3 dispersion model in Fortran with Python bindings

torch-dftd:

PyTorch implementation of DFT-D2 and DFT-D3

dispax:

Implementation of the DFT-D3 dispersion model in Jax M.D.

Installation

pip

The project can easily be installed with pip.

pip install tad-dftd3

From source

This project is hosted on GitHub at dftd3/tad-dftd3. Obtain the source by cloning the repository with

git clone https://github.com/dftd3/tad-dftd3
cd tad-dftd3

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.yml
mamba activate torch

For development, install the following additional dependencies

mamba install black coverage covdefaults mypy pre-commit pylint pytest tox

Install this project with pip in the environment

pip install .

Add the option -e for installing in development mode.

The following dependencies are required

• numpy

• torch

• pytest (tests only)

You can check your installation by running the test suite with pytest

pytest tests/ --pyargs tad_dftd3

or tox for testing multiple Python versions

tox

Example

The following example shows how to calculate the DFT-D3 dispersion energy for a single structure.

import torch
import tad_dftd3 as d3

numbers = d3.util.to_number(symbols="C C C C N C S H H H H H".split())
positions = torch.tensor(
    [
        [-2.56745685564671, -0.02509985979910, 0.00000000000000],
        [-1.39177582455797, +2.27696188880014, 0.00000000000000],
        [+1.27784995624894, +2.45107479759386, 0.00000000000000],
        [+2.62801937615793, +0.25927727028120, 0.00000000000000],
        [+1.41097033661123, -1.99890996077412, 0.00000000000000],
        [-1.17186102298849, -2.34220576284180, 0.00000000000000],
        [-2.39505990368378, -5.22635838332362, 0.00000000000000],
        [+2.41961980455457, -3.62158019253045, 0.00000000000000],
        [-2.51744374846065, +3.98181713686746, 0.00000000000000],
        [+2.24269048384775, +4.24389473203647, 0.00000000000000],
        [+4.66488984573956, +0.17907568006409, 0.00000000000000],
        [-4.60044244782237, -0.17794734637413, 0.00000000000000],
    ]
)
param = {
    "a1": torch.tensor(0.49484001),
    "s8": torch.tensor(0.78981345),
    "a2": torch.tensor(5.73083694),
}

energy = d3.dftd3(numbers, positions, param)

torch.set_printoptions(precision=10)
print(energy)
# tensor([-0.0004075971, -0.0003940886, -0.0003817684, -0.0003949536,
#         -0.0003577212, -0.0004110279, -0.0005385976, -0.0001808242,
#         -0.0001563670, -0.0001503394, -0.0001577045, -0.0001764488])

The next example shows the calculation of dispersion energies for a batch of structures, while retaining access to all intermediates used for calculating the dispersion energy.

import torch
import tad_dftd3 as d3

sample1 = dict(
    numbers=d3.util.to_number("Pb H H H H Bi H H H".split()),
    positions=torch.tensor(
        [
            [-0.00000020988889, -4.98043478877778, +0.00000000000000],
            [+3.06964045311111, -6.06324400177778, +0.00000000000000],
            [-1.53482054188889, -6.06324400177778, -2.65838526500000],
            [-1.53482054188889, -6.06324400177778, +2.65838526500000],
            [-0.00000020988889, -1.72196703577778, +0.00000000000000],
            [-0.00000020988889, +4.77334244722222, +0.00000000000000],
            [+1.35700257511111, +6.70626379422222, -2.35039772300000],
            [-2.71400388988889, +6.70626379422222, +0.00000000000000],
            [+1.35700257511111, +6.70626379422222, +2.35039772300000],
        ]
    ),
)
sample2 = dict(
    numbers=d3.util.to_number("C C C C C C I H H H H H S H C H H H".split(" ")),
    positions=torch.tensor(
        [
            [-1.42754169820131, -1.50508961850828, -1.93430551124333],
            [+1.19860572924150, -1.66299114873979, -2.03189643761298],
            [+2.65876001301880, +0.37736955363609, -1.23426391650599],
            [+1.50963368042358, +2.57230374419743, -0.34128058818180],
            [-1.12092277855371, +2.71045691257517, -0.25246348639234],
            [-2.60071517756218, +0.67879949508239, -1.04550707592673],
            [-2.86169588073340, +5.99660765711210, +1.08394899986031],
            [+2.09930989272956, -3.36144811062374, -2.72237695164263],
            [+2.64405246349916, +4.15317840474646, +0.27856972788526],
            [+4.69864865613751, +0.26922271535391, -1.30274048619151],
            [-4.63786461351839, +0.79856258572808, -0.96906659938432],
            [-2.57447518692275, -3.08132039046931, -2.54875517521577],
            [-5.88211879210329, 11.88491819358157, +2.31866455902233],
            [-8.18022701418703, 10.95619984550779, +1.83940856333092],
            [-5.08172874482867, 12.66714386256482, -0.92419491629867],
            [-3.18311711399702, 13.44626574330220, -0.86977613647871],
            [-5.07177399637298, 10.99164969235585, -2.10739192258756],
            [-6.35955320518616, 14.08073002965080, -1.68204314084441],
        ]
    ),
)
numbers = d3.util.pack(
    (
        sample1["numbers"],
        sample2["numbers"],
    )
)
positions = d3.util.pack(
    (
        sample1["positions"],
        sample2["positions"],
    )
)
ref = d3.reference.Reference()
rcov = d3.data.covalent_rad_d3[numbers]
rvdw = d3.data.vdw_rad_d3[numbers.unsqueeze(-1), numbers.unsqueeze(-2)]
r4r2 = d3.data.sqrt_z_r4_over_r2[numbers]
param = {
    "a1": torch.tensor(0.49484001),
    "s8": torch.tensor(0.78981345),
    "a2": torch.tensor(5.73083694),
}

cn = d3.ncoord.coordination_number(numbers, positions, rcov, d3.ncoord.exp_count)
weights = d3.model.weight_references(numbers, cn, ref, d3.model.gaussian_weight)
c6 = d3.model.atomic_c6(numbers, weights, ref)
energy = d3.disp.dispersion(
    numbers, positions, c6, rvdw, r4r2, d3.disp.rational_damping, **param
)

torch.set_printoptions(precision=10)
print(torch.sum(energy, dim=-1))
# tensor([-0.0014092578, -0.0057840119])

Contributing

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

License

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.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions.