Machine learning at the atomic-scale

TL;DR

This paper reviews recent advances in applying machine learning to atomic-scale modeling, emphasizing robust representations, regression methods, and insights into structure-property relationships.

Contribution

It provides a comprehensive overview of machine learning techniques for atomic-scale modeling, highlighting new methods for representing atomic configurations and constructing surrogate models.

Findings

Effective atomic configuration representations improve model robustness.

Regression algorithms enable accurate predictions of atomic properties.

Machine learning models offer insights into physical structure-property relations.

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.