Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y₂O₃ interface-an important model interface for cladding applications in nuclear fission reactors-by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies (ESeg) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining ESeg. Moreover, the segregation behavior at a specific Fe/Y₂O₃ interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y₂O₃ interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations.