Neural evolution structure generation: High Entropy Alloys

TL;DR

This paper introduces Neural Evolution Structures (NESs), a method combining neural networks and evolutionary algorithms to efficiently generate large, diverse High Entropy Alloy structures with significantly reduced computational costs.

Contribution

The paper presents a novel inverse design approach that enables rapid generation of large HEA structures using neural evolution, surpassing traditional methods in speed and flexibility.

Findings

Achieves ~1000x speed-up over SQSs

Can generate structures with over 40,000 atoms in hours

Allows multiple structure generations with the same composition

Abstract

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.