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Quantum Machine Learning in Chemistry and Materials

  • Bing Huang
  • Nadine O. Symonds
  • O. Anatole von Lilienfeld
Living reference work entry

Abstract

Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schrödinger equation. The increasing availability of large quantum mechanics reference datasets has enabled these developments. We review the basic theories and key ingredients of popular QML models such as choice of regressor, data of varying trustworthiness, the role of the representation, and the effect of training set selection. Throughout we emphasize the indispensable role of learning curves when it comes to the comparative assessment of different QML models.

Notes

Acknowledgements

We acknowledge support by the Swiss National Science foundation (No. PP00P2_138932, 407540_167186 NFP 75 Big Data, 200021_175747, NCCR MARVEL).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bing Huang
    • 1
  • Nadine O. Symonds
    • 1
  • O. Anatole von Lilienfeld
    • 1
  1. 1.Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL)University of BaselBaselSwitzerland

Section editors and affiliations

  • Nicola Marzari
    • 1
  1. 1.Laboratory of theory and simulation of materialsSwiss Federal Institute of TechnologyLausanneSwitzerland

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