The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API.

中文翻译：

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材料电化学系列


电化学系列是电化学中的有用工具，但其在材料化学中的有效性受到以下事实的限制：标准电化学系列基于相对较小的一组反应，其中许多反应是在水溶液中测量的。为了解决这个问题，我们使用机器学习从无机晶体结构数据库中的数万个条目创建了无机材料的电化学系列。我们证明，与基于水离子的系列相比，该系列通常与固态材料中的氧化态更加一致。该电化学系列是通过开发和参数化材料中氧化态的物理、人类可解释的模型而构建的。我们证明，该模型能够以比最先进的基于变压器的神经网络模型更准确的方式根据成分预测氧化态。我们介绍了我们的方法在结构预测、材料发现和材料电化学方面的应用，并讨论了可能的其他应用和需要改进的领域。为了方便使用我们的方法，我们引入了免费提供的网站和 API。