Deep Learning-Enhanced Titanium Potential Models for Accurate Molecular Dynamics

Abstract

Deep potential (DP) is an advanced deep learning-based method that can extract complex material interactions from density functional theory (DFT) calculations to construct highly accurate potential energy functions. Among various material systems, the transition metal titanium (Ti) has garnered significant attention in fields such as energy storage and conversion and catalysis, due to its unique electronic structure and catalytic activity. However, the complex interatomic interactions of Ti pose challenges for traditional potential models, making accurate descriptions difficult. To address this challenge, we developed a DP method capable of effectively establishing the interatomic potential functions required for molecular dynamics (MD) simulations of Ti, thereby enhancing simulation accuracy. This study systematically compares the predictions of the DP model with results from DFT, demonstrating that the DP model achieves DFT-level accuracy in predicting key properties. These properties include crystal structural characteristics, defect properties, surface and interface properties, as well as mechanical properties. The α-phase Hexagonal Close-Packed structure automatically transforms into the β-phase Body-Centered Cubic structure at 1189 K, exhibiting a melting temperature of 1864 K. These values closely align with the experimental phase transition temperature of 1209 K and the melting point of 1941 K, confirming the DP model’s accuracy under high-temperature conditions. Increasing temperature enhances Ti atom diffusivity, with a self-diffusion coefficient observed at the melting temperature of 2.51 × 10-10 m2/s, matching those of other Face-Centered Cubic metals. The successful application of this method lays a foundation for multiscale simulations and performance predictions of complex materials, with potential to advance high-throughput screening and precise modeling of new materials.