Abstract: Cu-Cr alloys are widely applied in electronic, aerospace and nuclear industries, due to their high strength and high conductivity. However, their terrible softening resistance limits wider applications. This paper presents a novel strategy of integrating mechanism features into interpretable machine learning (ML) to develop softening-resistant Cu-Cr alloys and to understand their mechanisms. First, the mechanism fea-tures were specially designed to describe mechanisms potentially vital to softening resistance, and they were obtained through first-principles calculations. Those mechanism features that described interfacial segregation and solute diffusion exhibited significant Gini importance during feature selection. Only inte-grated with them, did ML models achieve great performance, accurate predictions, and successful devel-opment of Cu-0.4Cr-0.10La/Ce (wt.%) alloys with excellent softening resistance. Then, the contributions of these mechanism features to the predictions were interpreted by a game theoretic approach, but unex-pectedly, they were not fully consistent with interpretations that we expected from mechanism features. Finally, investigation targeted at these inconsistencies gave novel insights into softening resistance mech-anisms. The Cu-Cr-La/Ce alloys' excellent softening resistance was not induced by a prevailing mechanism of La/Ce atoms segregating at phase interfaces, nor by an expected mechanism of La/Ce atoms improv-ing the Cr atom jump energy barriers. Instead, it was caused by a unique mechanism in which La/Ce atoms competed with Cr atoms for vacancies and therefore depleted the available vacancies for the Cr atom jump. This paper demonstrates a new paradigm of developing softening-resistant Cu-Cr alloys and understanding their mechanisms via mechanism-informed interpretable ML.

Key words: Copper alloys, Machine learning, Interfacial segregation, Solute diffusion