Grain boundary segregation prediction with a dual-solute model

TL;DR

This paper introduces a dual-solute spectral model that inherently accounts for solute interactions, significantly improving the accuracy of grain boundary segregation predictions in various alloy systems compared to traditional single-solute models.

Contribution

The paper develops and validates a dual-solute spectral model that naturally incorporates solute-solute interactions for more accurate grain boundary segregation predictions.

Findings

Dual-solute model outperforms single-solute models in accuracy.

Model validated in Al-Mg and other binary alloy systems.

Accurately predicts segregation states before secondary phase formation.

Abstract

Solute segregation along grain boundaries (GBs) profoundly affects their thermodynamic and kinetic behaviors in polycrystalline materials. Recently, the spectral approach has emerged as a powerful tool to predict GB segregation. However, previous GB segregation predictions using this method relied heavily on single-solute segregation energy spectrum without solute-solute interactions, which were often incorporated through a fitting parameter. In this work, we developed a dual-solute model whose segregation energy spectrum intrinsically incorporates solute-solute interactions. It was first validated for GB segregation prediction in the Al-Mg system and then extended to several other distinct binary alloy systems. The dual-solute model shows significant improvement over the single-solute model and can accurately predict the real segregation states obtained by hybrid Molecular Dynamics/Monte Carlo simulations within a broad temperature range with different solute concentrations before forming secondary phases. This dual-solute model provides an effective method for accurately predicting GB segregation in nanocrystalline metals.