Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
A machine learning–based classification approach for phase diagram prediction
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A fine-tuned Mistral LLM answers multiple-choice and short-answer questions on binary and ternary alloy phase diagrams and generates novel diagrams from component inputs alone.
citing papers explorer
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Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
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aLLoyM: A large language model for alloy phase diagram prediction
A fine-tuned Mistral LLM answers multiple-choice and short-answer questions on binary and ternary alloy phase diagrams and generates novel diagrams from component inputs alone.