Another class of machine-learned interatomic potential is the Gaussian approximation potential (GAP), which combines compact descriptors of local atomic environments with Gaussian process regression to machine learn the potential energy surface of a given system. To date, the GAP framework has been used to successfully develop a number of MLIPs for various systems, including for elemental systems such as Carbon Silicon, and Tungsten, as well as for multicomponent systems such as Ge 2 Sb 2 Te 5 and austenitic stainless steel, Fe 7 Cr 2 Ni.