  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.
