Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions

Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet the identification of competent substrates and their synthesis optimization remains challenging. Here, we report an integrated approach combining machine learning (ML) and large language models (LLMs) to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set. The resulting ML models, one for reactivity prediction and the other one for site selectivity, both achieved high accuracy (>90%) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions of substrates of interest upon the screening, LLMs were prompted to generate code to iteratively improve yield, lowering the barrier for scientists to access ML programs, and this strategy efficiently identified high-yield conditions for eight drug-like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 10 different LLMs, including llama, Claude, and GPT-4, on generating and executing codes related to ML based on natural language prompts given by chemists to showcase their tool-making and tool-using capabilities and potentials for accelerating research across four diverse tasks. In addition, we collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C-H oxidation reactions, and our findings revealed that integrating LLMs and ML outperformed using either method alone. We envision that this combined approach offers a robust and generalizable pathway for advancing synthetic chemistry research