Installing the program

Installing binaries

Binaries are available for the three main operating systems:

  • Windows

  • MacOS

  • Linux

With Conda

Conda is an open-source package and environment management system that runs on Windows, MacOS, and Linux. The conda repository contains a large number of open-source certified packages enabling scientific work. It is recommended that you install the minimal installer for conda named miniconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others.

Retrieve miniconda from the following website

Install the version for 64 bit computers that comes with Python (>=3.6).

Start a conda terminal, or Anaconda Powershell as it is referred to on a Windows system. Conda supports multiple environments and you start in the one named base as is typically indicated by the prompt. To create a new and additional environment named vlxenv and install VeloxChem, Matplotlib, and Jupyter notebook (and package dependencies such as NumPy and SciPy) into it, you enter the following command line statement

$ conda create -n vlxenv veloxchem matplotlib jupyterlab -c veloxchem -c conda-forge

You can list your conda environments

$ conda env list

The activated environment will be marked with an asterisk (the base environment to begin with) and you can activate your new environment with the command

$ conda activate vlxenv

as should be indicated by getting a modified prompt.

Inside this newly created environment, you should now be ready to start a Jupyter notebook with the command

$ jupyter-notebook

which should open in your default web browser. A notebook allows for interactive execution of Python code written into cells. You should now be able to import the VeloxChem module in a cell:

import veloxchem as vlx

and start calculations. See the eChem book for a multitude of examples.

Installing from source

Obtaining the source code

The source code can be downloaded from the GitLab repository:

$ git clone

Build prerequisites

  • C++ compiler fully compliant with the C++11 standard

  • Installation of Python >=3.6 that includes the interpreter, the development header files, and the development libraries

  • Build tool providing the make utility

  • The pybind11 (>=2.6) header-only library

  • MPI library

  • Linear algebra libraries implementing the BLAS and LAPACK interfaces

  • The following Python modules:

Optional, add-on dependencies:

See external-dependencies for instructions on how to get these add-ons.

To avoid clashes between dependencies, we recommend to always use a virtual enviroment.

With Anaconda

Anaconda and the software packaged on the conda-forge channel provide build isolation and greatly simplify the installation of VeloxChem.

  • Move to the folder containing the source code:

    $ cd veloxchem
  • Create and activate the Conda environment:

    $ conda create --name <name> python=3.x
    $ conda activate <name>


    VeloxChem requires at least Python 3.6

  • Install standard build dependencies:

    $ conda env update --file <environment> --prune

    This will install, among other, the C++ compiler and NumPy. The environment.yml file specifies the packages needed. They will be installed from the conda-forge channel. VeloxChem can use different linear algebra backends and we provide two environment files you can use to set up your conda environment:

    • mkl_env.yml which installs the Intel Math Kernel Library,

    • openblas_env.yml which installs the OpenBLAS library.

  • Install MPI and Mpi4Py

    • To use OpenMPI

      $ conda install openmpi mpi4py -c conda-forge
    • To use MPICH

       $ conda install mpich mpi4py -c conda-forge
  • Build and install VeloxChem in the Conda environment:

    $ python -m pip install .

    By default, the build process will use all available cores to compile the C++ sources in parallel. This behavior can be controlled via the VLX_NUM_BUILD_JOBS environment variable:

    $ env VLX_NUM_BUILD_JOBS=N python -m pip install .

    will install VeloxChem using N parallel processes.

  • The environment now contains all that is necessary to run VeloxChem. You can deactivate it by issuing:

    $ conda deactivate

Cray platform (x86-64 or ARM processor)

  • Load cray modules:

    $ module swap PrgEnv-cray PrgEnv-gnu
    $ module load cray-hdf5
    $ module load cray-python/
  • Create and activate a virtual enviroment

    $ python3 -m venv vlx
    $ source vlx/bin/activate
    $ python -m pip install -U pip

    Once the virtual environment is activated, you can just use python to invoke the interpreter.

  • Install Mpi4Py:

    1. Download a recent version of the source distribution::

      $ curl -LO
    2. Unzip the archive:

      $ tar xf mpi4py-X.Y.Z.tar.gz
      $ cd mpi4py-X.Y.Z
    3. Append the following lines to mpi.cfg:

      mpicc         = cc
      mpicxx        = CC
      extra_compile_args   = -shared
      extra_link_args      = -Wl,-rpath,/opt/cray/pe/mpt/7.7.9/gni/mpich-gnu/8.2/lib
    4. Build and install Mpi4Py:

      $ python build --mpi=cray
      $ python install
  • Use the compiler wrapper to compile VeloxChem:

    $ export CXX=CC
    $ cd veloxchem
    $ python -m pip install .

    This will also take care of installing the additional necessary Python modules.

Debian-based Linux

  • Install Intel Math Kernel Library from

    Note that this requires superuser privileges:

    $ wget
    $ apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
    $ sudo sh -c 'echo deb all main > /etc/apt/sources.list.d/intel-mkl.list'
    $ sudo apt-get update
    $ sudo apt-get install intel-mkl-64bit
  • Install MPI and Python:

     $ sudo apt-get install mpich python3 python3-dev python3-pip
  • Create and activate a virtual enviroment

    $ python3 -m venv vlx
    $ source vlx/bin/activate
    $ python -m pip install -U pip
  • Install VeloxChem:

    $ cd veloxchem
    $ python -m pip install .

RPM-based Linux

  • Install Math Kernel Library from

    Note that this requires superuser privileges:

    $ sudo yum-config-manager --add-repo
    $ sudo rpm --import
    $ sudo yum install intel-mkl-64bit
  • Install MPI and Python:

    $ sudo yum install mpich-3.2-devel python3-devel
    $ sudo ln -s /usr/lib64/mpich-3.2/bin/mpirun /usr/bin/mpirun
    $ sudo ln -s /usr/lib64/mpich-3.2/bin/mpicxx /usr/bin/mpicxx
    $ sudo ln -s /usr/lib64/mpich-3.2/bin/mpicc /usr/bin/mpicc
  • Create and activate a virtual enviroment

    $ python3 -m venv vlx
    $ source vlx/bin/activate
    $ python -m pip install -U pip
  • Install VeloxChem:

    $ cd veloxchem
    $ python -m pip install .




  • Soon to come!


If you wish to use functionality offered through interfaces with other software packages, you will first need to install them. Currently, interfaces to add-on dependencies XTB and CPPE (v0.2.1) are available.

The CPPE library for polarizable embedding

There are few ways to install the CPPE library for polarizable embedding. Note that you will need a C++ compiler compliant with the C++14 standard and CMake.

You can install it via pip in your virtual environment:

$ python -m pip install cppe==0.2.1

or as an extra during compilation of VeloxChem:

$ python -m pip install .[qmmm]

Alternatively, you can compile it without using pip:

# Build CPPE
$ git clone -b v0.2.1
$ cd cppe; mkdir build; cd build
$ make

# Set up python path
$ export PYTHONPATH=/path/to/your/cppe/build/stage/lib:$PYTHONPATH

The XTB package for semiempirical methods

It is recommended to install the XTB package in a conda environment:

$ conda install xtb -c conda-forge

Alternatively, you can compile it using cmake:

# Build XTB
$ git clone -b v6.3.3
$ cd xtb; mkdir build; cd build
$ cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=/path/to/your/xtb ..
$ make
$ make install

# Set XTBHOME prior to installing VeloxChem
$ export XTBHOME=/path/to/your/xtb

Release versions

  • 1.0-rc2 (2021-04-23) Second release candidate

  • 1.0-rc (2020-02-28) First release candidate