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

Efficient Data Selection Methods for the Development of Machine Learned Potentials