Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C–C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol⁻¹ and substantially better than SQM methods without ML correction (5.71 kcal mol⁻¹). Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved via the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.

中文翻译：

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机器学习和半经验计算：快速、准确和基于机制的反应势垒预测的协同方法

现代 QM 建模方法，如 DFT，为无数反应提供了详细的机制见解。然而，它们的计算成本抑制了它们在反应发现中快速筛选大量底物和催化剂的能力。对于形成硝基迈克尔加成的 C-C 键，我们引入了一种协同半经验量子力学 (SQM) 和机器学习 (ML) 方法，即使在标准笔记本电脑上使用广泛使用的建模软件。获得的平均绝对误差 (MAE) 低于公认的 1 kcal mol ^-1化学准确度阈值，并且明显优于没有 ML 校正的 SQM 方法 (5.71 kcal mol ^-1）。当 ML 模型应用于毒理学文献中未见的一组化合物时，预测能力被证明是有效的。还通过生成完整的 SQM 过渡态 (TS) 结构来实现机械洞察，这些结构被发现是 DFT 级几何形状的非常好的近似，揭示了一些 TS 中的重要空间相互作用。这种速度、准确性和机械洞察力的结合是前所未有的。当前的 ML 障碍模型至少在这些重要标准之一上妥协。