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

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.

KEYWORDS:

• high-entropy alloys
• genetic algorithm
• thermodynamics
• sampling algorithms
• machine learning

Acknowledgments

The author is thankful to Dr Colin Freeman for helping in the development of the protocol and Prof. Graeme Ackland for the critical feedback. The author is also thankful to the National Supercomputing Facility for access to the Param Siddhi AI system.

Disclosure statement

No potential conflict of interest was reported by the author.