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High-Entropy Materials are composed of multiple elements on comparativelysimpler lattices. Due to the multicomponent nature of such materials, theatomic scale sampling is computationally expensive due to the combinatorialcomplexity. We propose a genetic algorithm based methodology for sampling suchcomplex chemically-disordered materials. Genetic Algorithm based AtomisticSampling Protocol (GAASP) variants can generate low and well as high-energystructures. GAASP low-energy variant in conjugation with metropolis criteriaavoids the premature convergence as well as ensures the detailed balancecondition. GAASP can be employed to generate the low-energy structures forthermodynamic predictions as well as diverse structures can be generated formachine learning applications.

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