  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.
