High-entropy materials are composed of multiple elements on comparatively simpler lattices. Due to the multi-component nature of such materials, atomic-scale sampling is computationally expensive due to the combinatorial complexity. This study proposes a genetic algorithm-based methodology for sampling such complex chemically disordered materials. Genetic Algorithm-based Atomistic Sampling Protocol (GAASP) variants can generate low as well as high-energy structures. GAASP low-energy variant in conjugation with metropolis criteria avoids premature convergence as well as ensures detailed balance condition. GAASP can be employed to generate low-energy structures for thermodynamic predictions, and diverse structures can be generated for machine-learning applications.
CITATION STYLE
Anand, G. (2023). GAASP: Genetic Algorithm-Based Atomistic Sampling Protocol for High-Entropy Materials. Materials and Manufacturing Processes, 38(16), 2044–2050. https://doi.org/10.1080/10426914.2023.2217909
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