Development of a TaN-Ce Machine Learning Potential and Its Application to Solid–Liquid Interface Simulations

This study develops a machine learning potential (MLP) based on the Moment Tensor Potential (MTP) method for the TaN-Ce system. This potential is employed to investigate the interfacial structure and wetting behavior between liquid Ce and solid TaN. Molecular dynamics (MDs) simulations reveal that liquid Ce exhibits significant wetting on the TaN surface at high temperatures. The interfacial region undergoes pre-melting and component interdiffusion, forming an amorphous transition layer. Nitrogen atoms display high diffusivity, leading to surface mass loss, while tantalum atoms demonstrate excellent thermal stability and penetration resistance. These findings provide theoretical support for the design of interfacial materials and corrosion control in high-temperature metallurgy.