dftd3 0.6.0

Python API of the DFT-D3 project

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
# => -0.029489232932494884

QCSchema 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)
# => -0.0004204244108151285

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-forge

Once the conda-forge channel has been enabled, this project can be installed with:

conda install dftd3-python

Now you are ready to use dftd3, check if you can import it with

>>> import dftd3
>>> from dftd3.libdftd3 import get_api_version
>>> get_api_version()
'0.6.0'

Building the extension module

To perform an out-of-tree build some version of s-dftd3 has to be available on your system and preferably findable by pkg-config. Try to find a s-dftd3 installation you build against first with

pkg-config --modversion s-dftd3

Adjust 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.

• 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

  • cffi

  • numpy

  • pkgconfig (setup only)

Make sure to have your C compiler set to the CC environment variable

export CC=gcc

Install 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/awvwgk/simple-dftd3/archive/refs/heads/main.zip#egg=dftd3-python&subdirectory=python"

Using 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 _build

The 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 _build