  Machine learning (ML) approaches enable large-scale atomistic simulations
with near-quantum-mechanical accuracy. With the growing availability of these
methods there arises a need for careful validation, particularly for physically
agnostic models - that is, for potentials which extract the nature of atomic
interactions from reference data. Here, we review the basic principles behind
ML potentials and their validation for atomic-scale materials modeling. We
discuss best practice in defining error metrics based on numerical performance
as well as physically guided validation. We give specific recommendations that
we hope will be useful for the wider community, including those researchers who
intend to use ML potentials for materials "off the shelf".
