Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset
Name
Angewandte Chemie - 2024 - Joung - Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale.pdf
Description
Published version
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2.55 MB
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Author(s)
Joung, Joonyoung F
Fong, Mun Hong
Roh, Jihye
Tu, Zhengkai
Bradshaw, John
Coley, Connor W
Date Issued
October 21, 2024
Journal
Angewandte Chemie
Publisher
Wiley
Citation
Joung, Joonyoung F, Fong, Mun Hong, Roh, Jihye, Tu, Zhengkai, Bradshaw, John et al. 2024. "Reproducing Reaction Mechanisms with Machine‐Learning Models Trained on a Large‐Scale Mechanistic Dataset." Angewandte Chemie, 136 (43).
Version
Final published version
Abstract
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1002/ange.202411296
