Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks

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. Oxidation states help chemists to understand the bonding, properties and reactivity of compounds, but they can be difficult to determine for metal ions in extended crystalline materials. Now, oxidation states manually assigned to metal–organic frameworks have been harvested from the Cambridge Structural Database and used to build a machine-learning model that predicts oxidation states in metal–organic frameworks with good accuracy.