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

2023/02/16 by G. Anand, Anand, G. · 1 citation
Materials Science · Earth and Planetary Sciences · Physics and Astronomy · #Machine Learning in Materials Science #nanoparticles nucleation surface interactions #Advanced Chemical Physics Studies

paper · pdf · doi:10.48550/arxiv.2302.08101

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.

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• Information Entropy Based Crystal Structure Prediction of Chemically Disordered Alloys via Graph Convolutional Neural Networks

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