As more data are introduced in the building of models of chemical reactivity, the mechanistic component can be reduced until ‘big data’ applications are reached. These methods no longer depend on underlying mechanistic hypotheses, potentially learning them implicitly through extensive data training. Reactivity models often focus on reaction barribers, but can also be trained to directly predict lab-relevant properties, such as yields or conditions. Calculations with a quantum-mechanical component are still preferred for quantitative predictions of reactivity. Although big data applications tend to be more qualitative, they have the advantage to be broadly applied to different kinds of reactions. There is a continuum of methods in between these extremes, such as methods that use quantum-derived data or descriptors in machine learning models. Here, we present an overview of the recent machine learning applications in the field of chemical reactivity from a mechanistic perspective. Starting with a summary of how reactivity questions are addressed by quantum-mechanical methods, we discuss methods that augment or replace quantum-based modelling with faster alternatives relying on machine learning.

随着在化学反应模型的构建中引入更多数据，可以减少机械成分，直到达到“大数据”应用。这些方法不再依赖于潜在的机械假设，有可能通过广泛的数据训练隐式地学习它们。反应性模型通常侧重于反应障碍，但也可以通过训练直接预测与实验室相关的特性，例如产量或条件。对于反应性的定量预测，使用量子力学成分的计算仍然是首选。虽然大数据应用更倾向于定性，但它们具有广泛应用于不同种类反应的优势。在这些极端之间存在一系列方法，例如在机器学习模型中使用量子衍生数据或描述符的方法。这里，我们从机制的角度概述了最近机器学习在化学反应领域的应用。从总结量子力学方法如何解决反应性问题开始，我们讨论了用依赖于机器学习的更快替代方法来增强或取代基于量子的建模的方法。