Bayerl, Dylan

Convergence acceleration in machine learning potentials for atomistic simulations

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory simulations without appreciably sacrificing accuracy of material property prediction.