Kevin Maik Jablonka¹, Daniele Ongari¹, Seyed Mohamad Moosavi¹, Berend Smit^1*

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: berend.smit@epfl.ch

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), https://doi.org/10.24435/materialscloud:2019.0085/v1

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
datapackage.zip MD5md5:06c443c12aaf2584dbdcadaf2144d13a	100.7 MiB	datapackage containing features, labels, CSD codes, feature names and pre-trained models.
README.txt MD5md5:c43b268e6071f1793417b125060f5b93	2.1 KiB	README.txt detailing file contents of the datapackage

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External references

Keywords

ERC MOF ML oxidation state LSMO EPFL