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

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