Machine Learning at the Atomic Scale

Authors

  • Félix Musil Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne
  • Michele Ceriotti Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne;, Email: michele.ceriotti@epfl.ch

DOI:

https://doi.org/10.2533/chimia.2019.972

Keywords:

Machine learning

Abstract

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condensed-phase systems. This short review summarizes recent progress in the field, focusing in particular on the problem of representing an atomic configuration in a mathematically robust and computationally efficient way. We also discuss some of the regression algorithms that have been used to construct surrogate models of atomic-scale properties. We then show examples of how the optimization of the machine-learning models can both incorporate and reveal insights onto the physical phenomena that underlie structure–property relations.

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Published

2019-12-18

How to Cite

[1]
F. Musil, M. Ceriotti, Chimia 2019, 73, 972, DOI: 10.2533/chimia.2019.972.