Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal–organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal–organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.

了解化合物和材料中金属中心的氧化态有助于理解它们的化学键合和性质。化学家已经开发出基于电子计数规则预测氧化态的理论，但这些理论可能无法描述扩展晶体系统（如金属有机框架）中的氧化态。在这里，我们建议使用机器学习模型，由化学家根据剑桥结构数据库中化学名称编码的作业进行训练，自动将氧化态分配给金属有机框架中的金属离子。在我们的方法中，只考虑金属中心周围的直接局部环境。我们表明该策略对实验不确定性具有鲁棒性，例如不正确的质子化、未结合的溶剂或键长的变化。