Designing for Cooperative Grain Boundary Segregation in Multicomponent Alloys

TL;DR

This paper introduces a novel cooperative approach to grain boundary segregation in multicomponent alloys, showing how different solutes can work together to optimize boundary properties, supported by theoretical modeling, machine learning, and experimental validation.

Contribution

It presents a new spectral theoretical framework and machine learning data for cooperative segregation, challenging the traditional competition-only paradigm.

Findings

Cooperative segregation can be engineered in alloys to improve properties.

Machine learning predicts co-segregation in over 700 alloys.

Experimental validation confirms the cooperative model's predictions.

Abstract

Tailoring the nanoscale distribution of chemical species at grain boundaries is a powerful method to dramatically influence the properties of polycrystalline materials. However, classical approaches to the problem have tacitly assumed that only competition is possible between solute species. In this paper, we show that solute elements can cooperate in the way they segregate to grain boundaries: in properly targeted alloys, the different chemical species cooperate to each fill complementary grain boundary sites disfavored by the other. By developing a theoretical "spectral" approach to this problem based on quantum-accurate grain boundary site distributions, we show how grain boundaries can be cooperatively alloyed, whether by depletion or enrichment. We provide machine-learned co-segregation information for over 700 ternary aluminum-based alloys, and experimentally validate the concept in one ternary alloy where co-segregation is not expected by prior models, but is expected based on the cooperative model.