Cluster Expansion by Transfer Learning from Empirical Potentials

Abstract

Cluster expansions provide effective representations of the potential energy landscape of multicomponent crystalline solids. Notwithstanding major advances in cluster expansion implementations, it remains computationally demanding to construct these expansions for systems of low dimension or with a large number of components, such as clusters, interfaces, and multimetallic alloys. We address these challenges by employing transfer learning to accelerate the computationally demanding step of generating configurational data from first principles. The proposed approach exploits Bayesian inference to incorporate prior knowledge from physics-based or machine-learning empirical potentials, enabling one to identify the most informative configurations within a dataset. The efficacy of the method is tested on face-centered cubic Pt:Ni binaries, yielding a two- to three-fold reduction in the number of first-principles calculations, while ensuring robust convergence of the energies with low statistical fluctuations