  To fill the gap between accurate (and expensive) ab initio calculations and
efficient atomistic simulations based on empirical interatomic potentials, a
new class of descriptions of atomic interactions has emerged and been widely
applied; i.e., machine learning potentials (MLPs). One recently developed type
of MLP is the Deep Potential (DP) method. In this review, we provide an
introduction to DP methods in computational materials science. The theory
underlying the DP method is presented along with a step-by-step introduction to
their development and use. We also review materials applications of DPs in a
wide range of materials systems. The DP Library provides a platform for the
development of DPs and a database of extant DPs. We discuss the accuracy and
efficiency of DPs compared with ab initio methods and empirical potentials.
