Bayesian Optimization of Catalysis With In-Context Learning

TL;DR

This paper introduces a novel approach that uses large language models for Bayesian optimization in materials discovery, enabling efficient catalyst design without explicit models or feature engineering.

Contribution

It extends in-context learning to regression with uncertainty estimation, allowing language-based Bayesian optimization directly in natural language space for materials science.

Findings

BO-ICL matches or outperforms Gaussian processes on benchmarks.

It identifies near-optimal catalysts within six iterations in live experiments.

The method operates effectively without structural or electronic descriptors.

Abstract

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.