  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.
