ArticleACS central science2026
Bayesian Optimization of Catalysis with In-Context Learning.
Article in ACS central science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The trial behind it
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Who cites it
17 citing papers in PubMed.
- Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework.ACS central science · 2026Article
- Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Bayesian Optimization of Catalysis with In-Context Learning.ACS central science · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Large language models as uncertainty-calibrated optimizers for experimental discovery.Nature machine intelligence · 2026Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Probing the limitations of multimodal language models for chemistry and materials research.Nature computational science · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists.Nature chemistry · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- From biomass waste to COnpj computational materials · 2025Article
- Bioinformatics and biomedical informatics with ChatGPT: Year one review.Quantitative biology (Beijing, China) · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- Article
- Augmenting large language models with chemistry tools.Nature machine intelligence · 2024Article
- Article
- 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.Digital discovery · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. 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 multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. 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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.