Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of interpretable predictions and limited generalization capability to other chemistry domains, such as radical and atmospheric chemistry. To address these challenges, we introduce a new reaction predictor system, RMechRP, that leverages contrastive learning in conjunction with mechanistic pathways, the most interpretable representation of chemical reactions. Specifically designed for radical reactions, RMechRP provides different levels of interpretation of chemical reactions. We develop and train multiple deep-learning models using RMechDB, a public database of radical reactions, to establish the first benchmark for predicting radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and interpretable predictions of radical reactions, and its potential for various applications in atmospheric chemistry.

翻译：基于深度学习的反应预测器经历了显著的架构演变。然而，它们对美国专利局反应的依赖导致缺乏可解释的预测，且对自由基化学与大气化学等其他化学领域的泛化能力有限。为解决上述挑战，我们提出了一种新的反应预测系统RMechRP，该系统利用对比学习并结合机理路径——化学反应最具可解释性的表征。RMechRP专门针对自由基反应设计，可提供不同层次的化学反应解释。我们利用公共自由基反应数据库RMechDB开发并训练了多个深度学习模型，从而建立了首个自由基反应预测基准。实验结果证明了RMechRP在提供精确且可解释的自由基反应预测方面的有效性，以及其在大气化学领域的广泛应用潜力。