In this new publication, we have combined atomistic simulations and machine learning to predict grain boundary segregation in magnesium alloys with unprecedented accuracy. By capturing diverse atomic environments and stress effects, the study reveals neodymium as a promising solute for grain boundary engineering. This breakthrough paves the way for stronger, lighter, and more durable magnesium alloys. The manuscript is now published: Xie et al., Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach, Journal of Magnesium and Alloys 13 (2025) 2636 [open access]
