ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- PNcsp+: A Periodic Number-Based Crystal Structure Prediction Method Enhanced by Machine Learning.Journal of chemical theory and computation · 2026Article
- Understanding and rationalizing the structure of ionic solids by computational application of Beck's extended coordination number rule.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2026Article
- Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- An electrochemical series for materials.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.Journal of chemical information and modeling · 2024Article
- Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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