Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach

Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach

Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the...

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Bibliographic Details
Main Authors: Zhuocheng Xie, Achraf Atila, Julien Guénolé, Sandra Korte-Kerzel, Talal Al-Samman, Ulrich Kerzel
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-06-01
Series:Journal of Magnesium and Alloys
Subjects:
Grain boundary segregation
Magnesium alloys
Atomistic simulation
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2213956725001124
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