Learning grain boundary segregation energy spectra in polycrystals

Abstract

Predicting segregation energies of alloy systems can be challenging even for a single grain boundary. Here the authors propose a machine-learning framework, which maps the local environments on a distribution of segregation energies, to predict segregation energies of alloy elements in polycrystalline materials.