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AI-powered classification of bimolecular reaction mechanisms from kinetic data

CORDIS · observation · Publication date unknown

et, such studies typically demand substantial time and resources and can still yield inconclusive results. These limitations create an urgent need for more efficient approaches, and the rapid rise of artificial intelligence tools developed across many fields is opening powerful new possibilities to meet that need. Proof-of-concept work from Larrosa and Burés shows the potential of AI-based models as powerful tools for assisting kinetic analyses and elucidating reaction mechanisms. In their 2023 Nature paper, they show the possibility of training a deep learning model with data containing a variety of kinetic profiles from several mechanisms and demonstrated up to 99.99% accuracy, even under conditions with simulated experimental errors. While this proof-of-concept work only focuses on a small set of 20 unimolecular reaction mechanisms, our goal is to develop a simple-to-use tool powered by artificial intelligence that predicts bimolecular reaction mechanisms from experimental kinetic data. This project builds on the strong basis established in the published preliminary work and seeks

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR