Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks

• URL: http://arxiv.org/abs/2604.16468v1
• Date: Thu, 09 Apr 2026 01:30:38 GMT
• Title: Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
• Abstract summary: We propose a physics-informed graph attention network (GAT) that learns element-aware representations and couples them with thermodynamic constraints.<n>Using about 25,000 equilibrium states generated with pycalphad, each composition-temperature point is represented as a four-node element graph with atomic fractions and elemental descriptors as node features.<n>The model combines graph attention, global pooling, and a multilayer perceptron to predict nine relevant phases.
• Score: 0.5729426778193398
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate phase equilibria are foundational to alloy design because they encode the underlying thermodynamics governing stability, transformations, and processing windows. However, while the CALculation of Phase Diagrams (CALPHAD) provides a rigorous thermodynamic framework, exploring multicomponent composition-temperature space remains computationally expensive and is typically limited to sparse section. To enable rapid phase mapping and alloy screening, we propose a physics-informed graph attention network (GAT) that learns element-aware representations and couples them with thermodynamic constraints for multi-label phase-set prediction in the Ag-Bi-Cu-Sn alloy system. Using about 25,000 equilibrium states generated with pycalphad, each composition-temperature point is represented as a four-node element graph with atomic fractions and elemental descriptors as node features. The model combines graph attention, global pooling, and a multilayer perceptron to predict nine relevant phases. To improve physical consistency, we incorporate thermodynamic constraints, applied as training penalties or as an inference-time projection. Across six binary and three ternary subsystems, the baseline model achieves a macro-F1 score of 0.951 and 93.98% exact-set match, while physics-informed decoding improves robustness and raises exact-set accuracy to about 96% on dense in-domain grids. The surrogate also generalizes to an unseen ternary section with 99.32% exact-set accuracy and to a quaternary section at 700 °C with 91.78% accuracy. These results demonstrate that attention-based graph learning coupled with thermodynamic constraint enforcement provides an effective and physically consistent surrogate for high-resolution phase mapping and extrapolative alloy screening.

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