We introduce chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions. We developed fast N-sulfonylimine multicomponent reactions for understanding reactivity and to generate training data. Accelerated reactivity mechanisms were investigated using density functional theory. Intuitive chemical features learned by the model accurately predicted heterogeneous reactivity of N-sulfonylimine with different carboxylic acids. Validation of the predictions shows that reaction outcome interpretation is useful for human chemists.

中文翻译：

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N-磺酰亚胺对快速多组分反应的加速反应机制和可解释的机器学习模型

我们引入了化学反应流程图，以帮助化学家使用在少量反应上训练的统计稳健的机器学习模型来解释反应结果。我们开发了快速N-磺酰亚胺多组分反应以了解反应性并生成训练数据。使用密度泛函理论研究了加速反应机制。模型学习的直观化学特征准确预测了N-磺酰亚胺与不同羧酸的异质反应性。预测的验证表明反应结果解释对人类化学家有用。