Modelling Surface Segregation in Compositionally Complex Alloys with Ab-Initio Accuracy

TL;DR

This paper introduces machine learning potentials capable of predicting surface segregation in complex alloys with near DFT accuracy, enabling detailed atomistic simulations that reveal unexpected segregation behaviors in multicomponent catalysts.

Contribution

The authors develop and validate machine learning potentials for accurate surface segregation modeling in multicomponent alloys, surpassing traditional methods.

Findings

Machine learning potentials achieve near DFT accuracy for surface segregation.

Unexpected Co segregation observed in a Co-Cu-Fe-Mo-Ni alloy.

Segregation behavior explained through transition-metal chemistry principles.

Abstract

Compositionally complex alloys or concentrated solid solutions are the latest frontier in catalyst design, but mixing different elements in one catalyst may result in surface segregation. Atomistic simulations can predict segregation patterns, but standard approaches based on mean-field models, cluster expansion, or classical interatomic potentials are often limited for the description of multicomponent alloys. We present machine learning potentials that can describe surface segregation with near DFT accuracy. The method is used to study a complex Co-Cu-Fe-Mo-Ni quinary alloy. For this alloy, an unexpected segregation of Co, which has a relatively high surface energy, is observed. We rationalize this surprising mechanism in terms of simple transition-metal chemistry.