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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.

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Fig. 1: Schematic representation of the featurization approach.
Fig. 2: Performance metrics for the assignment of oxidation states of Cu in MOFs.
Fig. 3: First two principal components for Cu sites.
Fig. 4: Predictive performance across the periodic table.
Fig. 5: Predictions of the oxidation states in mixed-valence MOF Cu(i/ii)–BTC.
Fig. 6: Predictions of the oxidation states in MIL-47 before (as-synthesized) and after activation.

Data availability

The feature matrices, labels and a pretrained model are deposited on the Materials Cloud archive ( The data that reproduce the plots shown in the main text can be found in a Code Ocean Capsule (

Code availability

Predictions for MOF structures can be performed using the oximachinerunner Python package (, which can be installed from PyPi. The code for parsing, featurization as well for the ML models is available on GitHub ( and and deposited on Zenodo (10.5281/zenodo.3567011, 10.5281/zenodo.3567274). The web app is hosted on the work section of Materials Cloud ( The code for this app, along with a Dockerfile, is also available on GitHub ( and deposited on Zenodo (10.5281/zenodo.3603606). The code used to generate the plots shown in the main text can be found in a Code Ocean capsule ( The code used to generate the structure graphics in the graphical abstract is available in ref. 67.


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This work was supported by a European Research Council (ERC) Advanced Grant (Grant Agreement No. 666983, MaGic), the Swiss National Science Foundation (SNSF) under Grant 200021_172759 and the National Center of Competence in Research (NCCR) through the Materials’ Revolution: Computational Design and Discovery of Novel Materials (MARVEL). We thank L. Talirz and the Materials Cloud team for feedback on the web app, the integration into AiiDA workflows for DFT optimization ( and A. Yakutovich for help with the integration in AiiDAlab. Moreover, we are grateful for all the feedback we received from chemists all over the world on the potential errors we found in their CSD entries.

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Authors and Affiliations



K.M.J. developed the ML workflows. D.O. carried out the bond valence sum analyses. B.S., S.M.M. and K.M.J. developed the featurization. All authors contributed to the analysis of the data and the writing of the article.

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Correspondence to Berend Smit.

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The authors declare no competing interests.

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Peer review information Nature Chemistry thanks Joshua Schrier, Vivek Sinha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–36, Discussion, Tables 1–34 and refs. 1–327.

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Jablonka, K.M., Ongari, D., Moosavi, S.M. et al. Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks. Nat. Chem. 13, 771–777 (2021).

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