Efficient Data Selection Methods for the Development of Machine Learned Potentials

Abstract

We present an investigation into data selection methods for the efficient sampling of configuration space as applied to the development of inter-atomic potentials for scale bridging in molecular dynamics (MD) simulations. This investigation suggests that the most efficient sampling techniques are those that incorporate information on an atomic level such as forces or atomic energies. Finally, we generate an inter-atomic potential for the a sodium chloride system using each data selection technique and find that the global selection methods result in non-physical simulations.