The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoretical chemistry. To this aim, we establish the ORedOx159 database, a comprehensive, homogeneous (with reference values stemming from density functional theory calculations), and reliable resource containing 318 one-electron reduction and oxidation reactions and featuring 159 large organic compounds. Subsequently, we provide an instructive overview of the good practice in machine learning and of commonly utilized machine learning models. We then assess their predictive performances on the ORedOx159 dataset through extensive analyses. Our simulations using descriptors that are computed in an almost instantaneous way result in a notable improvement in prediction accuracy, with mean absolute error (MAE) values equal to 5.6 kcal mol${}^{-1}$ for reduction and 7.2 kcal mol${}^{-1}$ for oxidation potentials, which paves a way toward efficient in silico design of new electrochemical systems.

中文翻译：

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通过基于图的机器学习方法预测氧化还原电位


氧化和还原电位的评估是各个化学领域的关键任务。然而，通过理论计算进行的准确预测是一项补充任务，有时也是实验测量的唯一替代方案，但可能往往是资源密集型且耗时的。本文通过应用机器学习技术来解决这一挑战，特别关注基于图的方法（例如图编辑距离、图核和图神经网络），对这些方法进行回顾以启发它们与理论化学的深层联系。为此，我们建立了 ORedOx159 数据库，这是一个全面、均质（参考值源自密度泛函理论计算）且可靠的资源，包含 318 个单电子还原和氧化反应，包含 159 个大有机化合物。随后，我们对机器学习的良好实践和常用的机器学习模型进行了有益的概述。然后，我们通过广泛的分析评估他们在 ORedOx159 数据集上的预测表现。我们使用几乎即时计算的描述符进行模拟，结果预测精度显着提高，平均绝对误差 (MAE) 值等于 5.6 kcal mol${}^{-1}$用于还原和 7.2 kcal mol${}^{-1}$氧化电位，这为新电化学系统的高效计算机设计铺平了道路。