Designing for cooperative grain boundary segregation in multicomponent alloys

Area of Science:

• Materials Science
• Nanotechnology
• Physical Chemistry

Background:

• Controlling nanoscale chemical distribution at grain boundaries is key to tailoring polycrystalline material properties.
• Traditional models assume solute elements compete for grain boundary sites, limiting alloy design.
• Understanding solute interactions at grain boundaries is crucial for developing advanced materials.

Purpose of the Study:

• To demonstrate that solute elements can cooperate in segregating to grain boundaries.
• To introduce a theoretical framework for understanding cooperative alloying at grain boundaries.
• To provide predictive tools and experimental validation for cooperative solute segregation.

Main Methods:

• Development of a theoretical
• spectral
• approach using quantum-accurate grain boundary site distributions.
• Application of machine learning to predict cosegregation behavior in ternary alloys.
• Experimental validation of cooperative alloying in a selected ternary aluminum alloy.

Main Results:

• Solute elements can cooperatively fill complementary grain boundary sites, contrary to classical competition models.
• The theoretical spectral approach quantifies cooperative and competitive segregation.
• Machine-learned models predict cosegregation for over 700 ternary aluminum-based alloys.
• Experimental results confirm cooperative cosegregation in an alloy not predicted by prior models.

Conclusions:

• Cooperative alloying at grain boundaries is a viable strategy for materials design.
• The developed theoretical and computational tools enable prediction and engineering of grain boundary chemistry.
• This work opens new avenues for designing high-performance polycrystalline materials through controlled solute interactions.