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.

Automated summary

Understanding the oxidation state of metal centres in compounds and materials is crucial for understanding their chemical bonding and properties. By using a machine-learning model trained on chemist-assigned chemical names in the Cambridge Structural Database, oxidation states can be automatically assigned to metal ions in metal-organic frameworks, with high accuracy and reliability. This approach demonstrates the ability to detect incorrect assignments in the database, showcasing how collective knowledge can be harnessed through machine learning.