Multi-objective optimization of interatomic potentials with application to MgO

Abstract

The parameterization of a functional form for an interatomic potential is treated as a problem in multi-objective optimization. An autonomous, machine-learning approach based on the identification of the Pareto hypersurface of errors in predicted properties allows the development of an ensemble of parameterizations with high materials fidelity and robustness. The efficacy of this approach is illustrated for the simple example of a Buckingham potential for MgO. This approach also provides a strong foundation for uncertainty quantification of potential parameterizations.