A learning scheme to predict atomic forces and accelerate materials simulations

Abstract

The behavior of an atom in a molecule, liquid or solid is governed by the force it experiences. If the dependence of this vectorial force on the atomic chemical environment can be \(learned\) efficiently with high-fidelity from benchmark reference results-using "big data" techniques, i.e., without resorting to actual functional forms-then this capability can be harnessed to enormously speed up \(in \ silico\) materials simulations. The present contribution provides several examples of how such a \(force\) field for Al can be used to go far beyond the length-scale and time-scale regimes accessible presently using quantum mechanical methods. It is argued that pathways are available to systematically and continuously improve the predictive capability of such a learned force field in an adaptive manner, and that this concept can be generalized to include multiple elements.