Dataset Information

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.

SUBMITTER: Fu N

PROVIDER: S-EPMC10558692 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models.

Fu Nihang N Hu Jeffrey J Feng Ying Y Morrison Gregory G Loye Hans-Conrad Zur HZ Hu Jianjun J

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20230807 28

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 ox ...[more]

PMID: 37551059

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Dataset Information

Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models.

Publications

Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models.

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