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Using collective knowledge to assign oxidation states

Kevin Maik Jablonka1, Daniele Ongari1, Seyed Mohamad Moosavi1, Berend Smit1*

1 Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques (ISIC), École Polytechnique Fédérale de Lausanne (EPFL), Sion, VS, Switzerland

* Corresponding authors emails:
DOI10.24435/materialscloud:2019.0085/v1 [version v1]

Publication date: Dec 11, 2019

How to cite this record

Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit, Using collective knowledge to assign oxidation states, Materials Cloud Archive 2019.0085/v1 (2019), doi: 10.24435/materialscloud:2019.0085/v1.


Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (>98%) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.

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100.7 MiB datapackage containing features, labels, CSD codes, feature names and pre-trained models.
2.1 KiB README.txt detailing file contents of the datapackage


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
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External references

Journal reference
K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, submitted, 2019.
Software (Code that can be used to generate the feature matrix. )
Software (Software that implements the code to train and test the models. )


ERC MOF ML oxidation state LSMO EPFL