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Many-body dispersion library

Project description


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Libmbd contains implementations of the many-body dispersion (MBD) method in several programming languages and frameworks:

  • The Fortran implementation is the reference, most advanced implementation, with support for analytical gradients and distributed parallelism, and additional functionality beyond the MBD method itself. It provides a low-level and a high-level Fortran API, and a C API. Furthermore, Python bindings to the C API are provided.
  • The Python/Numpy implementation is intended for prototyping, and as a high-level language reference.
  • The Python/Tensorflow implementation is an experiment that should enable rapid prototyping of machine learning applications with MBD.

The Python-based implementations as well as Python bindings to the Libmbd C API are accessible from the Python package called Pymbd.

Installing Pymbd

The easiest way to get Pymbd is to install the Pymbd Conda package, which ships with pre-built Libmbd.

conda install -c libmbd pymbd

Alternatively, if you have Libmbd installed on your system (see below), you can install Pymbd via Pip, in which case it links against the installed Libmbd. To support Libmbd built with ScaLAPACK/MPI, the MPI extras is required.

pip install pymbd  # or pymbd[MPI]

In both cases, tests can be run with Pytest.

pytest -v --durations=3 --pyargs pymbd

If you don’t need the Fortran bindings in Pymbd, you can install it without the C extension, in which case pymbd.fortran becomes unimportable

pip install pymbd --install-option="--no-ext"

Installing Libmbd

Libmbd uses CMake for building and installation, and requires a Fortran compiler, LAPACK, and optionally ScaLAPACK/MPI.

On Ubuntu:

apt-get install gfortran libblas-dev liblapack-dev [mpi-default-dev mpi-default-bin libscalapack-mpi-dev]

On macOS:

brew install gcc [open-mpi scalapack]

The building and installation can then proceed with

git clone && cd libmbd
mkdir build && cd build
make install

This installs the Libmbd shared library, C API header file, and high-level Fortran API module file.

Tests can be run with

make check


from pymbd import mbd_energy_species, ang
from pymbd.fortran import MBDCalc

ene_py = mbd_energy_species(  # pure Python implementation
    [(0, 0, 0), (0, 0, 4*ang)], ['Ar', 'Ar'], [1, 1], 0.83
with MBDCalc() as calc:
    ene_f = calc.mbd_energy_species(  # Fortran implementation
        [(0, 0, 0), (0, 0, 4*ang)], ['Ar', 'Ar'], [1, 1], 0.83
assert abs(ene_f-ene_py) < 1e-15
use mbd, only: mbd_input_t, mbd_calc_t

type(mbd_input_t) :: inp
type(mbd_calc_t) :: calc
real(8) :: energy, gradients(3, 2)
integer :: code
character(200) :: origin, msg

inp%atom_types = ['Ar', 'Ar']
inp%coords = reshape([0d0, 0d0, 0d0, 0d0, 0d0, 7.5d0], [3, 2])
inp%xc = 'pbe'
call calc%init(inp)
call calc%get_exception(code, origin, msg)
if (code > 0) then
    print *, msg
end if
call calc%update_vdw_params_from_ratios([0.98d0, 0.98d0])
call calc%evaluate_vdw_method(energy)
call calc%get_gradients(gradients)
call calc%destroy()



For development, Libmbd doesn't have to be installed on the system, and Pymbd can be linked against Libmbd in the build directory. Use Tox for comfortable running of Python tests.

pip install tox tox-venv  # or just make sure you have tox and tox-venv installed
git clone && cd libmbd
make setup  # creates ./build and runs cmake
# do some development
make test && tox
# do some development
make test && tox

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