Investigating interfacial segregation of Ω/Al in Al–Cu alloys: A comprehensive study using density functional theory and machine learning

• Published in: Acta materialia 2024-10, Vol.279, p.120294, Article 120294
• Main Authors: Liu, Yu, Zhang, Yin, Xiao, Namin, Li, Xingwu, Dai, Fu-Zhi, Chen, Mohan
• Format: Article
• Language: English
• Subjects: Al–Cu alloys; Correlation analysis; Density functional theory; First-principles calculations; Machine learning; Solute segregation
• ISSN: 1359-6454
• DOI: 10.1016/j.actamat.2024.120294

Solute segregation at the interface between the aluminum (Al) matrix and the Ω (Al2Cu) phase decreases the interfacial energy, impedes the coarsening of precipitates, and enhances the thermal stability of such precipitates. In this study, we employ density functional theory to systematically calculate solute segregation energies of 42 solute elements at the coherent and semi-coherent interfaces between the two phases, as well as mixing energies of these elements within the Al and Cu sublattices of the Ω phase. Using correlation analysis and machine learning methods, we establish the relationship between the solute segregation energy and 20 selected atomic descriptors. Metalloid and late transition metal elements are predicted as potential candidates for enhancing the thermal stability of Al–Cu alloys. We observe that the solute segregation energy at the interfacial site of the semi-coherent interface correlates with the atomic size of solute atoms and their solubilities within the Ω phase. The developed machine learning models exhibit the potential to predict solute segregation energies at various sites of the coherent and semi-coherent interfaces. Overall, our study provides valuable insights into the stabilizing potential of individual elements at the Ω/Al interface in Al–Cu alloys. [Display omitted]