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Arxiv
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164
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ChemAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning
- A new approach called ChemAgent has been proposed to enhance Large Language Models (LLMs) for chemistry and materials science tasks.
- ChemAgent integrates 137 external chemical tools and a dataset curation pipeline to address challenges faced by LLMs, such as outdated pretraining knowledge and lack of specialized chemical expertise.
- The approach utilizes a Hierarchical Evolutionary Monte Carlo Tree Search (HE-MCTS) framework for tool planning and execution optimization, enabling step-level fine-tuning of the policy model.
- Experimental evaluations show that ChemAgent significantly improves performance in Chemistry QA and discovery tasks, providing a robust solution for integrating specialized tools with LLMs in advanced chemical applications.
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