Efficient global structure optimization with a machine learned surrogate model

Abstract

We propose a scheme for global optimization with first-principles energy expressions (GOFEE) of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local relaxations (exploitation) and further structural searches (exploration). Assuming Gaussian Processes, an acquisition function is used to decide on which of the resulting structures is the more promising. Subsequently, a single point first-principles energy calculation is conducted for that structure. The method is demonstrated to outperform by two orders of magnitude a well established first-principles based evolutionary algorithm in finding surface reconstructions. Finally, GOFEE is utilized to identify initial stages of the edge oxidation and oxygen intercalation of graphene sheets on the Ir(111) surface.

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