Bayesian calibration of interatomic potentials for binary alloys

TL;DR

This paper applies Bayesian calibration to fit embedded atom method potentials for binary alloys, enabling quantification of uncertainties and improving predictive reliability in atomistic simulations.

Contribution

It introduces a Bayesian framework for calibrating interatomic potentials, incorporating uncertainty quantification into the modeling process.

Findings

Bayesian calibration effectively updates model parameters with training data.

Uncertainty quantification reveals limitations of the potential.

Statistical modeling highlights importance of accounting for model errors.

Abstract

Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from semi-empirical considerations and contain unknown parameters that must be fitted based on training data. In the present work, we investigate Bayesian calibration as a means of fitting EAM potentials for binary alloys. The Bayesian setting naturally assimilates probabilistic assertions about uncertain quantities. In this way, uncertainties about model parameters and model errors can be updated by conditioning on the training data and then carried through to prediction. We apply these techniques to investigate an EAM potential for a family of gold-copper systems in which the training data correspond to density-functional theory values for lattice parameters, mixing enthalpies, and various elastic constants. Through the use of predictive distributions, we demonstrate the limitations of the potential and highlight the importance of statistical formulations for model error.