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

TL;DR

The paper introduces abICS, an open-source toolkit that combines first-principles calculations, machine learning, and advanced sampling techniques to simulate disorder in complex multi-component materials.

Contribution

It presents a novel software framework that integrates active learning with high-throughput ab initio calculations for efficient sampling of disordered multi-component systems.

Findings

Successfully applied to ionic systems and interfaces for energy applications.

Demonstrates efficient sampling of intermediate disorder levels.

Showcases the power of combining machine learning with ab initio methods.

Abstract

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.