GOCIA: a grand canonical global optimizer for clusters, interfaces, and adsorbates

Abstract

Restructuring of surfaces and interfaces plays a key role in the activation and/or deactivation of a wide spectrum of heterogeneous catalysts and functional materials. The statistical ensemble representation can provide unique atomistic insights into this fluxional and metastable realm, but constructing the ensemble is very challenging, especially for the systems with off-stoichiometric reconstruction and varying coverage of mixed adsorbates. Here, we report GOCIA, a versatile global optimizer for exploring the chemical space of these systems. It features the grand canonical genetic algorithm (GCGA), which bases the target function on the grand potential and evolves across the compositional space, as well as many useful functionalities, with implementation details explained. GOCIA has been applied to various systems in catalysis, from clusters to surfaces and from thermal to electrocatalysis.