Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design
Henry Sprueill, Carl Edwards, Mariefel Olarte, Udishnu Sanyal, Heng Ji, Sutanay Choudhury
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.- Anthology ID:
- 2023.findings-emnlp.560
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8348–8365
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.560
- DOI:
- 10.18653/v1/2023.findings-emnlp.560
- Cite (ACL):
- Henry Sprueill, Carl Edwards, Mariefel Olarte, Udishnu Sanyal, Heng Ji, and Sutanay Choudhury. 2023. Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8348–8365, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design (Sprueill et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-emnlp.560.pdf