AIMatDesign uses difference-based data augmentation, LLM-guided model refinement, and knowledge-based rewards to propose Zr-based bulk metallic glasses, with one experimentally validated alloy reaching 1.7 GPa yield strength and 10.2% elongation.
Bayesian optimization for materials design,
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AIMatDesign: Knowledge-Augmented Reinforcement Learning for Inverse Materials Design under Data Scarcity
AIMatDesign uses difference-based data augmentation, LLM-guided model refinement, and knowledge-based rewards to propose Zr-based bulk metallic glasses, with one experimentally validated alloy reaching 1.7 GPa yield strength and 10.2% elongation.