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

ABSTRACT: Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. Here, we report an integrated approach combining machine learning and large language models 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 for reactivity prediction achieved high accuracy (>90 %) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions for selected substrates, LLMs were promp

SUBMITTER: Zheng Z 

PROVIDER: S-EPMC11795713 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature