Cu-Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and TEM Study.

The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)45° missing row reconstruction (MRR). ETEM experiments validate these predictions and show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of CuO in MRR regions. Our approach based on combining disparate computational components and ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems.