Neural evolution structure generation: High entropy alloys
CGT Feugmo and K Ryczko and A Anand and CV Singh and I Tamblyn, JOURNAL OF CHEMICAL PHYSICS, 155, 044102 (2021).
We propose a neural evolution structure (NES) generation methodology combining artificial neural networks and evolutionary algorithms to generate high entropy alloy structures. Our inverse design approach is based on pair distribution functions and atomic properties and allows one to train a model on smaller unit cells and then generate a larger cell. With a speed-up factor of similar to 1000 with respect to the special quasi-random structures (SQSs), the NESs dramatically reduce computational costs and time, making possible the generation of very large structures (over 40 000 atoms) in few hours. Additionally, unlike the SQSs, the same model can be used to generate multiple structures with the same fractional composition.
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