Machine learning interatomic potential for high throughput screening and optimization of high-entropy alloys

TL;DR

This paper introduces a machine learning interatomic potential that enables rapid and accurate prediction of mechanical properties of high-entropy alloys, facilitating high throughput screening and optimization of alloy compositions.

Contribution

The authors developed a novel MLIP for high-entropy alloys that significantly accelerates property calculations and guides alloy design, outperforming traditional DFT methods in efficiency.

Findings

MLIP predictions align well with DFT and experimental data.

High throughput optimization identified promising alloy compositions.

Studied Ti concentration effects on mechanical properties.

Abstract

We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys (HEAs). MLIPs enabled accurate high throughput calculations of elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time consuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases and are consistent with the available experimental data. The MLIPs are also utilized for high throughput optimization of non equimolar R4 candidates by guided iterative tuning of R4 compositions to discover candidate materials with promising hardness-ductility combinations. We also used this approach to study the effect of Ti concentration on the elastic and mechanical properties of R4, by statistically averaging the properties of over 100 random structures. MLIP predicted hardness and bulk modulus of equimolar R4 and R5 HEAs are validated using experimentally measured Vickers hardness and modulus. This approach opens a new avenue for employing MLIPs for HEA candidate optimization.