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Deep learning-based reaction predictors have undergone significantarchitectural evolution. However, their reliance on reactions from the USPatent Office results in a lack of interpretable predictions and limitedgeneralization capability to other chemistry domains, such as radical andatmospheric chemistry. To address these challenges, we introduce a new reactionpredictor system, RMechRP, that leverages contrastive learning in conjunctionwith mechanistic pathways, the most interpretable representation of chemicalreactions. Specifically designed for radical reactions, RMechRP providesdifferent levels of interpretation of chemical reactions. We develop and trainmultiple deep-learning models using RMechDB, a public database of radicalreactions, to establish the first benchmark for predicting radical reactions.Our results demonstrate the effectiveness of RMechRP in providing accurate andinterpretable predictions of radical reactions, and its potential for variousapplications in atmospheric chemistry.

AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning