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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Out-of-Distribution Phase Equilibrium Prediction Using Physics-Informed Graph Attention Networks
Author(s) Eunjeong Park, Amrita Basak
On-Site Speaker (Planned) Amrita Basak
Abstract Scope Accurate phase equilibria are foundational to alloy design because they encode the thermodynamics governing stability, transformations, and processing windows. However, although the CALculation of Phase Diagrams (CALPHAD) framework provides a rigorous thermodynamic basis, exploration of multicomponent composition-temperature space remains computationally expensive and is typically limited to sparse regions of the full domain. This creates a key challenge for surrogate models: reliable prediction under distribution shift to unseen compositions and phase regions. To address this, a physics-informed graph attention network (GAT) is developed for multi-label phase-set prediction in the Ag-Bi-Cu-Sn alloy system. Each composition–temperature point is represented as an element graph with atomic fractions and elemental descriptors as node features. The model combines graph attention and global pooling to predict equilibrium phase sets. The model achieves strong performance on binary and ternary subsystems and demonstrates robust out-of-distribution generalization under significant distribution shift.
Proceedings Inclusion? Planned:
Keywords Machine Learning,

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