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Discovering novel catalysts requires complex reasoning involving multiplechemical properties and resultant trade-offs, leading to a combinatorial growthin the search space. While large language models (LLM) have demonstrated novelcapabilities for chemistry through complex instruction following capabilitiesand high quality reasoning, a goal-driven combinatorial search using LLMs hasnot been explored in detail. In this work, we present a Monte Carlo TreeSearch-based approach that improves beyond state-of-the-art chain-of-thoughtprompting variants to augment scientific reasoning. We introduce two newreasoning datasets: 1) a curation of computational chemistry simulations, and2) diverse questions written by catalysis researchers for reasoning about novelchemical conversion processes. We improve over the best baseline by 25.8\% andfind that our approach can augment scientist's reasoning and discovery processwith novel insights.

Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design