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

• URL: http://arxiv.org/abs/2310.14420v1
• Date: Sun, 22 Oct 2023 21:29:33 GMT
• Title: Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design
• Authors: Henry W. Sprueill, Carl Edwards, Mariefel V. Olarte, Udishnu Sanyal, Heng Ji, Sutanay Choudhury
• Abstract summary: Large language models (LLM) have demonstrated novel capabilities for chemistry through complex instruction following capabilities and high quality reasoning. 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.
• Score: 42.3838742984173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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