Fine-tuning large language models to generate single-atom catalyst synthesis procedures

Heterogeneous catalyst design is primarily expert-driven and difficult to keep track amidst rapidly growing literature, motivating the use of large language models (LLMs) to learn from existing knowledge. While generalist LLMs show some capability in generating synthesis procedures, they struggle on encountering complex catalytic systems like single-atom catalysts (SAC). Here, we investigate fine-tuning of LLMs to improve the generation of SAC synthesis procedures with chemical relevance. We curate a dataset of 2,964 SAC publications and train the Granite-based LLM to recommend protocols based on user-defined prompts including metal-support combinations, synthesis method, and target reactions. We demonstrate the model's practicality through a user interface allowing researchers to query procedures tailored to their design conditions. Human-in-the-loop validation of 150 model-generated SAC protocols confirmed coherent, context-aware procedures with synthesis sequence steps that align with experimental logic, along with limitations. We underscore LLM-generated protocols must be critically reviewed and used as assistive tools, and highlight fine-tuned LLMs for AI-assisted synthesis planning driving innovation in catalysis research.