Understanding grain boundary segregation and solute drag using computational and machine learning studies

Grain boundary (GB) solute segregation has been used as a strategy to tailor the properties and processing pathways of a wide range of metallic alloys. GB solute drag results when segregated alloying elements exert a resistive force on migrating GBs hindering their motion. While GB segregation has been the subject of active research, a detailed understanding of solute drag and the migration kinetics of doped boundaries is still lacking, especially in technologically -relevant alloys. Through theoretical analysis, mesoscale modeling, and machine learning studies, we investigate GB segregation and solute drag and establish design maps relating drag effects to relevant alloy and GB properties, i.e., the complete alloy design space. We find that solute drag is dominant in immiscible alloys with far -from -dilute compositions in agreement with experimental observations of GB segregation in metallic alloys. Our analysis reveals that solute-solute interactions within the GB and the degree of segregation asymmetry greatly influence solute drag values. In broad terms, our work provides future avenues to employ GB segregation to control boundary dynamics during materials processing or under service conditions.