Abstract
ABSTRACT 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.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
