QML is a Python2/3-compatible toolkit for representation learning of properties of molecules and solids. QML supplies the the building blocks to carry out efficient and accurate machine learning on chemical compounds. The goal is to provide usable and efficient implementations of concepts such as representations and kernels as well as a high level interface to make it easy for computational chemists to use these for machine-learning tasks.
- Anders S. Christensen (University of Basel)
- Lars A. Bratholm (University of Bristol)
- Silvia Amabilino (University of Bristol)
- Jimmy C. Kromann (University of Basel)
- Felix A. Faber (University of Basel)
- Bing Huang (University of Basel)
- David R. Glowacki (University of Bristol)
- Alexandre Tkatchenko (University of Luxembourg)
- Klaus-Robert Müller (Technische Universitat Berlin/Korea University)
- O. Anatole von Lilienfeld (University of Basel)
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.
Until the preprint is available from arXiv, please cite use of QML as:
AS Christensen, LA Bratholm, S Amabilino, JC Kromann, FA Faber, B Huang, GR Glowacki, A Tkatchenko, K.R. Muller, OA von Lilienfeld (2018) "QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml
For citation of the individual procedures of QML, please see the "Citing use of QML" section.
.. toctree:: :maxdepth: 2 :caption: GETTING STARTED: :name: index installation citation tutorial qml_examples/examples
.. toctree:: :maxdepth: 2 :caption: SOURCE DOCUMENTATION: :name: qml qml
QML is freely available under the terms of the MIT license.