@article{Musil_Ceriotti_2019, title={Machine Learning at the Atomic Scale}, volume={73}, url={https://www.chimia.ch/chimia/article/view/2019_972}, DOI={10.2533/chimia.2019.972}, abstractNote={ 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. }, number={12}, journal={CHIMIA}, author={Musil, Félix and Ceriotti, Michele}, year={2019}, month={Dec.}, pages={972} }