We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) 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 approximately 1000 with respect to the SQSs, the NESs dramatically reduces 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.
Cite
@article{arxiv.2103.01462,
title = {Neural evolution structure generation: High Entropy Alloys},
author = {Conrard Giresse Tetsassi Feugmo and Kevin Ryczko and Abu Anand and Chandra Veer Singh and Isaac Tamblyn},
journal= {arXiv preprint arXiv:2103.01462},
year = {2021}
}