arXiv (OAI Expanded)
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Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents
Inverse design of metal-organic frameworks (MOFs) requires navigating combinatorial spaces with costly property labels and opaque machine-learning models. We introduce LLM4MOF, a closed-loop multi-agent framework that converts a natural-language target into chemical hypotheses, constraints, diagnost
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