Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

TL;DR

This study introduces a machine-learning potential-based computational method to analyze phase stability and transitions in high-entropy alloys, revealing the crucial role of lattice relaxations and discovering a new chemical ordering mechanism.

Contribution

The paper presents a novel machine-learning approach combined with Monte Carlo simulations to efficiently study phase behavior in complex high-entropy alloys, emphasizing the importance of local relaxation effects.

Findings

Lattice relaxations significantly stabilize single-phase bcc NbMoTaW.

The approach accurately predicts phase transitions and stability.

A new relaxation-driven chemical ordering mechanism is identified.

Abstract

Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.