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Bayesian Optimization of Catalysis With In-Context Learning

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arxiv 2304.05341 v2 pith:LW7HEY5X submitted 2023-04-11 physics.chem-ph cs.LG

Bayesian Optimization of Catalysis With In-Context Learning

Mayk Caldas Ramos , Shane S. Michtavy , Marc D. Porosoff , Andrew D. White This is my paper
classification physics.chem-ph cs.LG
keywords languagematerialsbo-iclcatalysisdiscoverybayesiancandidatesgaussian
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-3.5, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing experimental catalyst synthesis and testing procedures as natural language prompts. A key challenge in materials discovery is the need to characterize suboptimal candidates, which slows progress. While BO is effective for navigating large design spaces, standard surrogate models like Gaussian processes assume smoothness and continuity, an assumption that fails in highly non-linear domains such as heterogeneous catalysis. Our task-agnostic BO workflow overcomes this by operating directly in language space, producing interpretable and actionable predictions without requiring structural or electronic descriptors. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water-gas shift (RWGS) reaction, BO-ICL identifies near-optimal multi-metallic catalysts within six iterations from a pool of 3,700 candidates. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI. Code: https://github.com/ur-whitelab/BO-ICL.

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Cited by 7 Pith papers

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