Influence of chemistry and structure on interfacial segregation in NbMoTaW with high-throughput atomistic simulations

TL;DR

This study uses high-throughput atomistic simulations and machine learning to analyze how local structure and chemistry influence interfacial segregation in NbMoTaW refractory alloys, revealing complex segregation patterns and the importance of chemical information.

Contribution

It introduces a large-scale simulation approach combined with machine learning to understand and predict segregation behavior in complex multi-principal element alloys.

Findings

Nb is the dominant segregant in grain boundaries.

Local structural and chemical factors can cause unexpected segregation events.

Including chemical features improves machine learning prediction accuracy.

Abstract

Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary regions with enhanced structural disorder. In this study, we use atomistic simulations of a large group of bicrystal models to sample a wide variety of interfacial sites (grain boundary) in NbMoTaW and explore emergent trends in interfacial segregation and the underlying structural and chemical driving factors. Sampling hundreds of bicrystals along the [001] symmetric tilt axis and analyzing more than one hundred and thirty thousand grain boundary sites with a variety of local atomic environments, we uncover segregation trends in NbMoTaW. While Nb is the dominant segregant, more notable are the segregation patterns that deviate from expected behavior and mark situations where local structural and chemical driving forces lead to interesting segregation events. For example, incomplete depletion of Ta in low-angle boundaries results from chemical pinning due to favorable local compositional environments associated with chemical short-range ordering. Finally, machine learning models capturing and comparing the structural and chemical features of interfacial sites are developed to weigh their relative importance and contributions to segregation tendency, revealing a significant increase in predictive capability when including local chemical information. Overall, this work, highlighting the complex interplay between local grain boundary structure and chemical short-range ordering, suggest tunable segregation and chemical ordering by tailoring grain boundary structure in multi-principal element alloys.