# Bayesian Optimization of Catalysis With In-Context Learning

arXiv: https://arxiv.org/abs/2304.05341 · HTML: https://mlsanity.com/paper/2304.05341/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 93 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Faithful Chain-of-Thought Reasoning](https://mlsanity.com/paper/2301.13379/) (2023)
- [Selective Annotation Makes Language Models Better Few-Shot Learners](https://mlsanity.com/paper/2209.01975/) (2022)
- [Large-Scale Chemical Language Representations Capture Molecular Structure and Properties](https://mlsanity.com/paper/2106.09553/) (2021)
- [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://mlsanity.com/paper/2205.11822/) (2022)
- [LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks](https://mlsanity.com/paper/2206.06565/) (2022)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Teaching Algorithmic Reasoning via In-context Learning](https://mlsanity.com/paper/2211.09066/) (2022)
- [SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery](https://mlsanity.com/paper/1911.04738/) (2019)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks](https://mlsanity.com/paper/2012.06051/) (2020)
- [Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification](https://mlsanity.com/paper/2109.10254/) (2021)
- [Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains](https://mlsanity.com/paper/2106.01309/) (2021)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery](https://mlsanity.com/paper/2002.06053/) (2020)
- [Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals](https://mlsanity.com/paper/1501.03854/) (2015)

## Built on by (most cited first, 22 of 22)

- [Large Language Models to Enhance Bayesian Optimization](https://mlsanity.com/paper/2402.03921/) (2024)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://mlsanity.com/paper/2406.10833/) (2024)
- [A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?](https://mlsanity.com/paper/2402.05015/) (2024)
- [Are large language models superhuman chemists?](https://mlsanity.com/paper/2404.01475/) (2024)
- [Transformers and Large Language Models for Chemistry and Drug Discovery](https://mlsanity.com/paper/2310.06083/) (2023)
- [Large language models as uncertainty-calibrated optimizers for experimental discovery](https://mlsanity.com/paper/2504.06265/) (2025)
- [Fine-tune Language Models to Approximate Unbiased In-context Learning](https://mlsanity.com/paper/2310.03331/) (2023)
- [MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time](https://mlsanity.com/paper/2508.08641/) (2025)
- [Holistic chemical evaluation reveals pitfalls in reaction prediction models](https://mlsanity.com/paper/2312.09004/) (2023)
- [Towards a Science Exocortex](https://mlsanity.com/paper/2406.17809/) (2024)
- [Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial](https://mlsanity.com/paper/2604.01328/) (2026)
- [Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactions](https://mlsanity.com/paper/2504.08874/) (2025)
- [Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation](https://mlsanity.com/paper/2508.05427/) (2025)
- [CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimization](https://mlsanity.com/paper/2606.14581/) (2026)
- [Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions](https://mlsanity.com/paper/2510.18911/) (2025)
- [Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework](https://mlsanity.com/paper/2602.21533/) (2026)
- [LUMINA: LLM-Guided GPU Architecture Exploration via Bottleneck Analysis](https://mlsanity.com/paper/2603.05904/) (2026)
- [Fine-Tuning Large Language Models for Quantum Reasoning](https://mlsanity.com/paper/2606.21974/) (2026)
- [Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search](https://mlsanity.com/paper/2608.15669/) (2026)
- [Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings](https://mlsanity.com/paper/2609.03177/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

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.