An Electrochemical Series for Materials

03 November 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. We have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this approach enables the prediction of oxidation states directly from composition in a way that is physically justified, human-interpretable, and more accurate than a state-of-the-art transformer-based neural network model. We present applications of our model to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available web site and API.

Keywords

Electrochemical series
Oxidation states
Machine learning
Materials discovery
Structure prediction
Materials chemistry
Inorganic materials
Electrochemistry

Supplementary materials

Title
Description
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Title
ICSD-derived electrochemical series
Description
The ICSD-derived electrochemical series presented in this work. This is the same information as provided in Extended Data Table 1.
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Entries removed during the cleaning process based on GII
Description
This is a list of the entries removed during the cleaning process based on the global instability index (GII), along with associated values. The columns of this file are described in Table S4.
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Structures used to represent polyatomic ions
Description
These are the structures used to represent polyatomic ions when searching for polyatomic ions in a material. The files are in xyz format, with units of angstroms.
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