A Multi-Level Monte Carlo Tree Search Method for Configuration Generation in Crystalline Systems

TL;DR

This paper introduces a multi-level Monte Carlo tree search algorithm to efficiently generate optimal atomic configurations in crystalline materials, addressing the combinatorial complexity and rugged energy landscapes.

Contribution

The paper presents a novel hierarchical Monte Carlo tree search method tailored for configuration generation in crystalline systems, improving exploration efficiency.

Findings

Demonstrates efficiency in identifying optimal configurations

Reduces redundancy through hierarchical decomposition

Accelerates exploration of complex configuration spaces

Abstract

In this paper, we study the construction of structural models for the description of substitutional defects in crystalline materials. Predicting and designing the atomic structures in such systems is highly challenging due to the combinatorial growth of atomic arrangements and the ruggedness of the associated landscape. We develop a multi-level Monte Carlo tree search algorithm to generate the "optimal" configuration within a supercell. Our method explores the configuration space with an expanding search tree through random sampling, which further incorporates a hierarchical decomposition of the crystalline structure to accelerate exploration and reduce redundancy. We perform numerical experiments on some typical crystalline systems to demonstrate the efficiency of our method in identifying optimal configurations.