Implementation of the DFT-D4 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 D4 dispersion model, see
- E. Caldeweyher, C. Bannwarth and S. Grimme, J. Chem. Phys., 2017, 147, 034112. DOI: 10.1063/1.4993215
- 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
- E. Caldeweyher, J.-M. Mewes, S. Ehlert and S. Grimme, Phys. Chem. Chem. Phys., 2020, 22, 8499-8512. DOI: 10.1039/D0CP00502A
For alternative implementations, also check out
- dftd4:
- Implementation of the DFT-D4 dispersion model in Fortran with Python bindings.
- cpp-d4:
- Implementation of the DFT-D4 dispersion model in C++.
tad-dftd4 can easily be installed with pip.
pip install tad-dftd4
This project is hosted on GitHub at dftd4/tad-dftd4. Obtain the source by cloning the repository with
git clone https://github.com/dftd4/tad-dftd4 cd tad-dftd4
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
For development, additionally install the following tools in your environment.
mamba install black covdefaults coverage mypy pre-commit pylint tox
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
The following example shows how to calculate the DFT-D4 dispersion energy for a single structure.
import torch
import tad_dftd4 as d4
numbers = d4.utils.to_number(symbols="C C C C N C S H H H H H".split())
# coordinates in Bohr
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],
]
)
# total charge of the system
charge = torch.tensor(0.0)
# TPSS0-D4-ATM parameters
param = {
"s6": positions.new_tensor(1.0),
"s8": positions.new_tensor(1.85897750),
"s9": positions.new_tensor(1.0),
"a1": positions.new_tensor(0.44286966),
"a2": positions.new_tensor(4.60230534),
}
energy = d4.dftd4(numbers, positions, charge, param)
torch.set_printoptions(precision=10)
print(energy)
# tensor([-0.0020841344, -0.0018971195, -0.0018107513, -0.0018305695,
# -0.0021737693, -0.0019484236, -0.0022788253, -0.0004080658,
# -0.0004261866, -0.0004199839, -0.0004280768, -0.0005108935])The next example shows the calculation of dispersion energies for a batch of structures.
import torch
import tad_dftd4 as d4
# S22 system 4: formamide dimer
numbers = d4.utils.pack((
d4.utils.to_number("C C N N H H H H H H O O".split()),
d4.utils.to_number("C O N H H H".split()),
))
# coordinates in Bohr
positions = d4.utils.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])
# TPSS0-D4-ATM parameters
param = {
"s6": positions.new_tensor(1.0),
"s8": positions.new_tensor(1.85897750),
"s9": positions.new_tensor(1.0),
"a1": positions.new_tensor(0.44286966),
"a2": positions.new_tensor(4.60230534),
}
# calculate dispersion energy in Hartree
energy = torch.sum(d4.dftd4(numbers, positions, charge, param), -1)
torch.set_printoptions(precision=10)
print(energy)
# tensor([-0.0088341432, -0.0027013607])
print(energy[0] - 2*energy[1])
# tensor(-0.0034314217)This is a volunteer open source projects and contributions are always welcome. Please, take a moment to read the contributing guidelines.
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