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Simulation of the intermediate levels of disorder found in multi-componentmulti-sublattice systems in various functional materials is a challengingissue, even for state-of-the-art methodologies based on first-principlescalculation. Here, we introduce our open-source package ab Initio ConfigurationSampling Toolkit (abICS), which combines high-throughput first-principlescalculations, machine learning, and parallel extended ensemble sampling in anactive learning setting to enable such simulations. The theoretical backgroundis reviewed in some detail followed by brief notes on usage of the software. Inaddition, our recent applications of abICS to multi-component ionic systems andtheir interfaces for energy applications are reviewed as demonstration of thepower of this approach.

Configuration sampling in multi-component multi-sublattice systems enabled by ab Initio Configuration Sampling Toolkit (abICS)