Quantum Chemistry Meets Machine Learning

Authors

  • Alberto Fabrizio Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne
  • Benjamin Meyer Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne
  • Raimon Fabregat Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne
  • Clemence Corminboeuf Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne;, Email: clemence.corminboeuf@epfl.ch

DOI:

https://doi.org/10.2533/chimia.2019.983

Keywords:

Catalysis, Free-energy landscapes, Machine learning, Quantum chemistry

Abstract

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

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Published

2019-12-18

How to Cite

[1]
A. Fabrizio, B. Meyer, R. Fabregat, C. Corminboeuf, Chimia 2019, 73, 983, DOI: 10.2533/chimia.2019.983.