Design and Experimental Validation of a Photocatalyst Recommender Based on a Large Language Model
Year: 2025
Type: article
Abstract: Utilizing an extensive library of literature on photocatalytic transformations, we disclose the development of a machine learning (ML) model for the recommendation of photocatalysts most suitable for reactions of interest. The model is trained on > 36 000 such literature examples and uses an architecture inspired by the Bidirectional Encoder Representations from Transformer (BERT) large language model. Under cross-validation, it can suggest the "correct" photocatalysts with ∼90% accuracy. When experimentally tested on five out-of-box reactions, this algorithm consistently suggested photocatalysts that gave yields competitive to those chosen by human researchers and frequently suggested alternative photocatalysts that are potentially more appealing than the originally selected photocatalyst. Altogether, this platform serves as a valuable tool for researchers undertaking reaction optimization programs. The model is free to use at https://photocatals.grzybowskigroup.pl/predict/.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,510
Awards: G1042849371, G1209599001, G1981394742, Industrial CASE Account - University of St Andrews 2021, G2395829288, SolOLED: TADF Dendrimers for Highly Efficient Solution-Processed OLEDs, Multi-resonance TADF materials for highly efficient and stable OLEDs, Photocatalysis as a tool for synthetic organic chemistry, Boron: Beyond the Reagent, G8524864788
OpenAlex ID: W4417158904