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Predicting co-segregation in alloys with solute-solute interactions
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DOI：10.1016/j.actamat.2026.122628.png)
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Abstract
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The co-segregation of impurities in systems with multiple solute species has been widely recognized as an effective strategy for tailoring material properties. However, reliable predictions of co-segregation behavior remain a significant challenge for alloy design in these systems. In this work, we develop an extended dual-solute (DS) segregation framework to semi-quantitatively predict co-segregation behavior with solute-solute interactions, including both homoatomic and heteroatomic contributions. A machine-learning workflow is first established to predict the pairwise segregation energies to construct the DS segregation energy spectra that intrinsically include both types of solute-solute interactions. The resulting spectral information is then used to determine the upper and lower bounds of segregation for individual solute species. When applied to magnesium-based ternary systems constructed by alloying Mg with any two of the 11 candidate solute species (Ag, Al, Ca, Co, Cu, Gd, Nd, Ni, Pb, Pd, and Zn), the extended DS segregation framework is successfully validated by hybrid molecular dynamics/Monte Carlo simulations and experimental results available in the literature. Furthermore, we introduce a design strategy to promote co-segregation by incorporating additional solute species that exhibit attractive interactions with existing solutes, thereby enabling enhanced co-segregation even in the presence of strong site competition. These results underscore the critical role of solute-solute interactions in governing co-segregation behavior and provide a predictive pathway for the design and optimization of alloys.
Keywords:
Grain boundary co-segregation
Machine learning
Atomistic simulations
Segregation energy spectrum
Solute-solute interactions
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