TY - JOUR AU - Musil, Félix AU - Ceriotti, Michele PY - 2019/12/18 Y2 - 2024/03/29 TI - Machine Learning at the Atomic Scale JF - CHIMIA JA - Chimia VL - 73 IS - 12 SE - Scientific Articles DO - 10.2533/chimia.2019.972 UR - https://www.chimia.ch/chimia/article/view/2019_972 SP - 972 AB - 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. ER -