A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions.
TL;DR
A machine-learning workflow that couples the crystal generator MatterGen with a fine-tuned MatterSim interatomic potential to expand the candidate phase space and compute temperature-dependent phase stability with accuracy approaching density functional theory is reported.
Abstract
Phase diagrams encode the thermodynamic equilibria that govern alloy processing, but finite-temperature construction remains slow because candidate phases must be identified and their Gibbs free energies evaluated accurately. We report a machine-learning workflow that couples the crystal generator MatterGen with a fine-tuned MatterSim interatomic potential to expand the candidate phase space and compute temperature-dependent phase stability with accuracy approaching density functional theory. As demonstrated for the Li-Ga-Sn ternary system, the workflow constructs 0 and 300 K Gibbs phase-equilibrium diagrams and predicts a temperature-induced switch near the 3Li-2Ga-2Sn composition from the {LiGaSn, LiGa, Li8Sn3} assemblage to {LiGaSn, LiGa, LiSn} at approximately 230 K. X-ray diffraction of two synthesized compositions supports the predicted room-temperature assemblages. The approach offers a practical route for scalable finite-temperature phase-diagram construction and thermodynamic screening of intermetallic systems.