[G] 相安定性・相変態

12:30 〜 12:45

[203] Machine Learning Interatomic Potential Calculations of Finite-Temperature Mixing Energy for CALPHAD Database Development

○Arkapol Saengdeejing¹, Ryoji Sahara¹ (1. NIMS)

キーワード：

CALPHAD、Thermodynamic modeling、machine learning interatomic potential、SQS

Universal MLIP (MatterSim) was used to calculate the mixing enthalpies and finite-temperature excess Gibbs free energies of disordered FCC, BCC, and HCP phases for 45 first-row transition-metal binary systems. The results are compared with CALPHAD databases, demonstrating the potential of MLIPs for rapid thermodynamic database development.