DFT-D3 Python API
Python interface for the D3 dispersion model. This Python project is targeted at developers who want to interface their project via Python with s-dftd3.
This interface provides access to the C-API of s-dftd3 via the CFFI module. The low-level CFFI interface is available in the dftd3.libdftd3 module and only required for implementing other interfaces. A more pythonic interface is provided in the dftd3.interface module which can be used to build more specific interfaces.
from dftd3.interface import RationalDampingParam, DispersionModel
import numpy as np
numbers = np.array([1, 1, 6, 5, 1, 15, 8, 17, 13, 15, 5, 1, 9, 15, 1, 15])
positions = np.array([ # Coordinates in Bohr
[+2.79274810283778, +3.82998228828316, -2.79287054959216],
[-1.43447454186833, +0.43418729987882, +5.53854345129809],
[-3.26268343665218, -2.50644032426151, -1.56631149351046],
[+2.14548759959147, -0.88798018953965, -2.24592534506187],
[-4.30233097423181, -3.93631518670031, -0.48930754109119],
[+0.06107643564880, -3.82467931731366, -2.22333344469482],
[+0.41168550401858, +0.58105573172764, +5.56854609916143],
[+4.41363836635653, +3.92515871809283, +2.57961724984000],
[+1.33707758998700, +1.40194471661647, +1.97530004949523],
[+3.08342709834868, +1.72520024666801, -4.42666116106828],
[-3.02346932078505, +0.04438199934191, -0.27636197425010],
[+1.11508390868455, -0.97617412809198, +6.25462847718180],
[+0.61938955433011, +2.17903547389232, -6.21279842416963],
[-2.67491681346835, +3.00175899761859, +1.05038813614845],
[-4.13181080289514, -2.34226739863660, -3.44356159392859],
[+2.85007173009739, -2.64884892757600, +0.71010806424206],
])
model = DispersionModel(numbers, positions)
res = model.get_dispersion(RationalDampingParam(method="pbe0"), grad=False)
print(res.get("energy")) # Results in atomic units
# => -0.029489232932494884QCSchema Integration
This Python API natively understands QCSchema and the QCArchive infrastructure. If the QCElemental package is installed the dftd3.qcschema module becomes importable and provides the run_qcschema function.
from dftd3.qcschema import run_qcschema
import qcelemental as qcel
atomic_input = qcel.models.AtomicInput(
molecule = qcel.models.Molecule(
symbols = ["O", "H", "H"],
geometry = [
0.00000000000000, 0.00000000000000, -0.73578586109551,
1.44183152868459, 0.00000000000000, 0.36789293054775,
-1.44183152868459, 0.00000000000000, 0.36789293054775
],
),
driver = "energy",
model = {
"method": "tpss",
},
keywords = {
"level_hint": "d3bj",
},
)
atomic_result = run_qcschema(atomic_input)
print(atomic_result.return_result)
# => -0.0004204244108151285ASE Integration
To integrate with ASE this interface implements an ASE Calculator. The DFTD3 calculator becomes importable if an ASE installation is available.
>>> from ase.build import molecule
>>> from dftd3.ase import DFTD3
>>> atoms = molecule('H2O')
>>> atoms.calc = DFTD3(method="TPSS", damping="d3bj")
>>> atoms.get_potential_energy()
-0.0114416338147162
>>> atoms.calc.set(method="PBE")
{'method': 'PBE'}
>>> atoms.get_potential_energy()
-0.009781913226281063
>>> atoms.get_forces()
array([[-0.00000000e+00 -0.00000000e+00 9.56568982e-05]
[-0.00000000e+00 -4.06046858e-05 -4.78284491e-05]
[-0.00000000e+00 4.06046858e-05 -4.78284491e-05]])To use the DFTD3 calculator as dispersion correction the calculator can be combined using the SumCalculator from the ase.calculators.mixing module.
>>> from ase.build import molecule
>>> from ase.calculators.mixing import SumCalculator
>>> from ase.calculators.nwchem import NWChem
>>> from dftd3.ase import DFTD3
>>> atoms = molecule('H2O')
>>> atoms.calc = SumCalculator([DFTD3(method="PBE", damping="d3bj"), NWChem(xc="PBE")])For convenience DFTD3 allows to combine itself with another calculator by using the add_calculator method which returns a SumCalculator:
>>> from ase.build import molecule
>>> from ase.calculators.emt import EMT
>>> from dftd3.ase import DFTD3
>>> atoms = molecule("C60")
>>> atoms.calc = DFTD3(method="pbe", damping="d3bj").add_calculator(EMT())
>>> atoms.get_potential_energy()
7.513593999944228
>>> [calc.get_potential_energy() for calc in atoms.calc.calcs]
[-4.850025823367818, 12.363619823312046]The individual contributions are available by iterating over the list of calculators in calc.calcs. Note that DFTD3 will always place itself as first calculator in the list.
PySCF support
Integration with PySCF is possible by using the dftd3.pyscf module. The module provides a DFTD3Dispersion class to construct a PySCF compatible calculator for evaluating the dispersion energy and gradients.
>>> from pyscf import gto
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
... atom="""
... C -0.189833176 -0.645396435 0.069807761
... C 1.121636324 -0.354065576 0.439096514
... C 1.486520953 0.962572632 0.712107225
... C 0.549329390 1.989209324 0.617868956
... C -0.757627135 1.681862630 0.246856908
... C -1.138190460 0.370551816 -0.028582325
... Br -2.038462778 3.070459841 0.115165429
... H 1.852935245 -1.146434699 0.514119204
... H 0.825048723 3.012176989 0.829385472
... H 2.502259769 1.196433556 1.000317333
... H -2.157140187 0.151608161 -0.313181471
... H -0.480820487 -1.664983631 -0.142918416
... S -4.157443472 5.729584377 -0.878761129
... H -4.823791426 4.796089466 -1.563433338
... C -2.828338520 5.970593053 -2.091189515
... H -2.167577293 6.722356639 -1.668621815
... H -2.264954814 5.054835899 -2.240198499
... H -3.218524904 6.337447714 -3.035087058
... """
... )
>>> d3 = disp.DFTD3Dispersion(mol, xc="PW6B95", version="d3bj")
>>> d3.kernel()[0]
array(-0.01009386)
>>> d3.version = "d3zero" # Change to zero damping
>>> d3.kernel()[0]
array(-0.00574098)
>>> d3.atm = True # Activate three-body dispersion
>>> d3.kernel()[0]
array(-0.00574289)To make use of the dispersion correction together with other calculators, the energy method allows to apply a dispersion correction to an existing calculator.
>>> from pyscf import gto, scf
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
... atom="""
... O -1.65542061 -0.12330038 0.00000000
... O 1.24621244 0.10268870 0.00000000
... H -0.70409026 0.03193167 0.00000000
... H -2.03867273 0.75372294 0.00000000
... H 1.57598558 -0.38252146 -0.75856129
... H 1.57598558 -0.38252146 0.75856129
... """
... )
>>> grad = disp.energy(scf.RHF(mol)).run().nuc_grad_method()
converged SCF energy = -149.947191000075
>>> g = grad.kernel()
--------------- DFTD3 gradients ---------------
x y z
0 O 0.0171886976 0.0506606246 0.0000000000
1 O 0.0383596853 -0.0459057549 0.0000000000
2 H -0.0313133974 -0.0125865676 -0.0000000000
3 H 0.0066705789 -0.0380501872 0.0000000000
4 H -0.0154527822 0.0229409425 0.0215141991
5 H -0.0154527822 0.0229409425 -0.0215141991
----------------------------------------------Installing
This project is packaged for the conda package manager and available on the conda-forge channel. To install the conda package manager we recommend the miniforge installer. If the conda-forge channel is not yet enabled, add it to your channels with
conda config --add channels conda-forgeOnce the conda-forge channel has been enabled, this project can be installed with:
conda install dftd3-pythonNow you are ready to use dftd3, check if you can import it with
>>> import dftd3
>>> from dftd3.library import get_api_version
>>> get_api_version()
'1.7.0'Building the extension module
The released Python source distribution bundles the native s-dftd3 sources. When building directly from the repository, an installed s-dftd3 must be available through CMake or pkg-config, depending on the selected build system. Try to find the installation first with
pkg-config --modversion s-dftd3Adjust the PKG_CONFIG_PATH environment variable to include the correct directories to find the installation if necessary.
Using pip
This project support installation with pip as an easy way to build the Python API. Precompiled Python wheels for Linux are available on pypi and can be installed with
pip install dftd3Other platforms need to build from source, the following dependencies are required to do so
C compiler to build the C-API and compile the extension module (the compiler name should be exported in the CC environment variable)
Python 3.6 or newer
The following Python packages are required additionally
Make sure to have your C compiler set to the CC environment variable
export CC=gccInstall the project with pip
pip install .If you already have a s-dftd3 installation, e.g. from conda-forge, you can build the Python extension module directly without cloning this repository
pip install "https://github.com/dftd3/simple-dftd3/archive/refs/heads/main.zip#egg=dftd3-python&subdirectory=python"Using CMake
This directory contains a standalone CMake build for the CFFI extension. It first looks for an installed s-dftd3 CMake package and then for a bundled subprojects/s-dftd3 source tree, as provided by the Python source distribution. It requires CMake 3.18 or newer and Python 3.8 or newer with cffi and setuptools installed.
cmake -S . -B _build -G Ninja -DPython3_EXECUTABLE=$(which python3)
cmake --build _build
cmake --install _build --prefix=/path/to/install --component pythonUsing meson
This directory contains a separate meson build file to allow the out-of-tree build of the CFFI extension module. The out-of-tree build requires
C compiler to build the C-API and compile the extension module
meson version 0.53 or newer
a build-system backend, i.e. ninja version 1.7 or newer
Python 3.6 or newer with the CFFI package installed
Setup a build with
meson setup _build -Dpython_version=$(which python3)The Python version can be used to select a different Python version, it defaults to 'python3'. Python 2 is not supported with this project, the Python version key is meant to select between several local Python 3 versions.
Compile the project with
meson compile -C _buildThe extension module is now available in _build/dftd3/_libdftd3.*.so. You can install as usual with
meson configure _build --prefix=/path/to/install
meson install -C _build2 maintainers
Metadata
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Release files for dftd3 1.7.0
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All 31 files in this release have been uploaded with provenance metadata.
Attestation for dftd3-1.7.0.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 28, 2026.
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