GAASP: Genetic Algorithm Based Atomistic Sampling Protocol for High-Entropy Materials

TL;DR

This paper introduces GAASP, a genetic algorithm-based method for efficiently sampling atomistic configurations in high-entropy materials, enabling accurate thermodynamic predictions and diverse structure generation for machine learning.

Contribution

The paper presents a novel genetic algorithm-based protocol specifically designed for sampling complex high-entropy materials, addressing computational challenges of multicomponent atomic configurations.

Findings

GAASP can generate low and high-energy structures effectively.

GAASP's low-energy variant avoids premature convergence using metropolis criteria.

GAASP enables thermodynamic predictions and diverse structure generation.

Abstract

High-Entropy Materials are composed of multiple elements on comparatively simpler lattices. Due to the multicomponent nature of such materials, the atomic scale sampling is computationally expensive due to the combinatorial complexity. We propose a genetic algorithm based methodology for sampling such complex chemically-disordered materials. Genetic Algorithm based Atomistic Sampling Protocol (GAASP) variants can generate low and well as high-energy structures. GAASP low-energy variant in conjugation with metropolis criteria avoids the premature convergence as well as ensures the detailed balance condition. GAASP can be employed to generate the low-energy structures for thermodynamic predictions as well as diverse structures can be generated for machine learning applications.