  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.
