Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties

Authors

  • Anders S. Christensen Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, CH-4056 Basel
  • O. Anatole von Lilienfeld Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, CH-4056 Basel;, Email: anatole.vonlilienfeld@unibas.ch

DOI:

https://doi.org/10.2533/chimia.2019.1028

Keywords:

Chemical space, Machine learning, Operators, Quantum chemistry, Response properties

Abstract

The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.

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Published

2019-12-18

How to Cite

[1]
A. S. Christensen, O. A. von Lilienfeld, Chimia 2019, 73, 1028, DOI: 10.2533/chimia.2019.1028.