Interstitials as a key ingredient for P segregation to grain boundaries in polycrystalline $\alpha$-Fe

TL;DR

This study investigates phosphorus segregation in polycrystalline alpha-iron, highlighting the significant role of interstitial sites in grain boundary enrichment, which was previously underexplored, using atomistic simulations and machine learning.

Contribution

It provides a comprehensive analysis of both substitutional and interstitial phosphorus segregation in alpha-Fe grain boundaries, emphasizing the importance of considering interstitial sites for accurate predictions.

Findings

Interstitial P segregation significantly contributes to grain boundary enrichment.

Substitutional sites are preferred for P segregation based on mean segregation energy.

A representative spectrum of segregation energies is crucial for accurate quantitative predictions.

Abstract

The segregation of solutes to grain boundaries can significantly influence material behavior. Most previous computational studies have concentrated on substitutional solute segregation, neglecting interstitial segregation due to its increased complexity. The site preference, interstitial or substitutional, for P segregation in $\alpha$-Fe still remains under debate. In this work, we investigate the full GB-segregation spectrum for both substitutional and interstitial GB sites in a polycrystalline atomistic structure of ferrite with the aid of classical interatomic potentials combined with machine learning techniques. The method is qualitatively tested for H and Ni, where the segregation behavior in $\alpha$-Fe is well understood. Our findings for P show that segregation to both types of GB sites is possible, with a preference for the substitutional sites based on the mean segregation energy. However, due to the much larger number of interstitial sites, interstitial segregation significantly contributes to the GB enrichment with P. This underscores the importance of considering interstitial P segregation in addition to the substitutional one. Furthermore, we also argue that equally important for quantitative predictions (that agree with experimental data) is to get a representative spectrum of the segregation energies.