Prediction of mechanical properties of non-equiatomic high-entropy alloy by atomistic simulation and machine learning

TL;DR

This paper combines molecular dynamics simulations and machine learning to predict mechanical properties of non-equiatomic high-entropy alloys, expanding understanding beyond traditional equiatomic compositions.

Contribution

It introduces a novel approach using MD and ML to predict properties of non-equiatomic HEAs, with a focus on model comparison and validation.

Findings

KELM outperforms other ML models in predicting yield stress and Young's modulus.

A database of 900 MD simulations was used for training and testing.

Model predictions were validated on large polycrystal samples.

Abstract

High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on equiatomic or near equiatomic HEAs. However, there is probably far more treasure in those non-equiatomic HEAs with carefully designed composition. In this study, molecular dynamics (MD) simulation combined with machine learning (ML) methods were used to predict the mechanical properties of non-equiatomic CuFeNiCrCo HEAs. A database was established based on a tensile test of 900 HEA single-crystal samples by MD simulation. We investigated and compared eight ML models for the learning tasks, ranging from shallow models to deep models. It was found that the kernel-based extreme learning machine (KELM) model outperformed others for the prediction of yield stress and Young's modulus. The accuracy of the KELM model was further verified by the large-sized polycrystal HEA samples.