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Quantum Machine Learning in Chemistry and Materials

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Handbook of Materials Modeling

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.

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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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Authors and Affiliations

  1. Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), University of Basel, Basel, Switzerland

    Bing Huang, Nadine O. Symonds & O. Anatole von Lilienfeld

Authors
  1. Bing Huang
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  2. Nadine O. Symonds
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  3. O. Anatole von Lilienfeld
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Corresponding author

Correspondence to O. Anatole von Lilienfeld .

Editor information

Editors and Affiliations

  1. Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering , Massachusetts Institute of Technology , Cambridge, Massachusetts, USA

    Sidney Yip

Section Editor information

  1. Laboratory of theory and simulation of materials, Swiss Federal Institute of Technology, MED 2 1126 (Batiment MED), Station 9 CH-1015, Lausanne, Switzerland

    Nicola Marzari

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Huang, B., Symonds, N.O., Lilienfeld, O.A.v. (2018). Quantum Machine Learning in Chemistry and Materials. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling . Springer, Cham. https://doi.org/10.1007/978-3-319-42913-7_67-1

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  • DOI: https://doi.org/10.1007/978-3-319-42913-7_67-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42913-7

  • Online ISBN: 978-3-319-42913-7

  • eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

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