  Simulation of the intermediate levels of disorder found in multi-component
multi-sublattice systems in various functional materials is a challenging
issue, even for state-of-the-art methodologies based on first-principles
calculation. Here, we introduce our open-source package ab Initio Configuration
Sampling Toolkit (abICS), which combines high-throughput first-principles
calculations, machine learning, and parallel extended ensemble sampling in an
active learning setting to enable such simulations. The theoretical background
is reviewed in some detail followed by brief notes on usage of the software. In
addition, our recent applications of abICS to multi-component ionic systems and
their interfaces for energy applications are reviewed as demonstration of the
power of this approach.
