  Discovering novel catalysts requires complex reasoning involving multiple
chemical properties and resultant trade-offs, leading to a combinatorial growth
in the search space. While large language models (LLM) have demonstrated novel
capabilities for chemistry through complex instruction following capabilities
and high quality reasoning, a goal-driven combinatorial search using LLMs has
not been explored in detail. In this work, we present a Monte Carlo Tree
Search-based approach that improves beyond state-of-the-art chain-of-thought
prompting variants to augment scientific reasoning. We introduce two new
reasoning datasets: 1) a curation of computational chemistry simulations, and
2) diverse questions written by catalysis researchers for reasoning about novel
chemical conversion processes. We improve over the best baseline by 25.8\% and
find that our approach can augment scientist's reasoning and discovery process
with novel insights.
