Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models
Abstract: Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation to their bonds. As a fundamental property, OS has been widely used in charge‐neutrality verification, crystal structure determination, and reaction estimation. Currently, only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition‐based oxidation state prediction still remains elusive so far, which has significant implications for the discovery of new materials for which the structures have not been determined. This work proposes a novel deep learning‐based BERT transformer language model BERTOS for predicting the oxidation states for all elements of inorganic compounds given only their chemical composition. This model achieves 96.82% accuracy for all‐element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61% accuracy for oxide materials. It is also demonstrated how it can be used to conduct large‐scale screening of hypothetical material compositions for materials discovery.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models ; day:07 ; month:08 ; year:2023 ; extent:11
Advanced science ; (07.08.2023) (gesamt 11)
- Creator
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Fu, Nihang
Hu, Jeffrey
Feng, Ying
Morrison, Gregory
Loye, Hans‐Conrad zur
Hu, Jianjun
- DOI
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10.1002/advs.202301011
- URN
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urn:nbn:de:101:1-2023080815122767780378
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:53 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Fu, Nihang
- Hu, Jeffrey
- Feng, Ying
- Morrison, Gregory
- Loye, Hans‐Conrad zur
- Hu, Jianjun
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