Build Status Pypi doi Beta

QML: A Python Toolkit for Quantum Machine Learning

QML is a Python2/3-compatible toolkit for representation learning of properties of molecules and solids. QML is not a high-level framework where you can do model.train(), but supplies the building blocks to carry out efficient and accurate machine learning on chemical compounds. As such, the goal is to provide usable and efficient implementations of concepts such as representations and kernels.

Current list of contributors:

  • Anders S. Christensen (University of Basel)
  • Lars A. Bratholm (University of Bristol)
  • Jimmy C. Kromann (University of Basel)
  • Silvia Amabilino (University of Bristol)
  • Felix A. Faber (University of Basel)
  • Bing Huang (University of Basel)
  • Alexandre Tkatchenko (University of Luxembourg)
  • Klaus-Robert Muller (Technische Universität Berlin/Korea University)
  • David R. Glowacki (University of Bristol)
    1. Anatole von Lilienfeld (University of Basel)

Code development

The QML code is developed through our GitHub repository:

Please add you code to QML by forking and making pull-requests to the “develop” branch. Every now and then develop branch is pushed to the “master” branch and automatically deployed to PyPI, where the latest stable version is hosted.

See the “Installing QML” page for up-to-date installation instructions.


QML is freely available under the terms of the MIT license.