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

• DOI: 10.1002/advs.202301011
• Publication Date: 2023-08-08T06:10:46Z
• Authors: Nihang Fu; Jeffrey Hu; Ying Feng; Gregory Morrison; Hans‐Conrad zur Loye; Jianjun Hu
• Keywords: Oxidation state

Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation 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 oxidation of given compound with many exceptions. Recent work developed machine learning models based structural features predicting metal ions. However, composition-based state prediction still remains elusive so far, which significant implications discovery new materials structures have not determined. This proposes novel deep learning-based BERT transformer language model BERTOS all elements inorganic compounds chemical composition. achieves 96.82% accuracy all-element benchmarked cleaned ICSD dataset 97.61% oxide materials. It is also demonstrated how it can be conduct large-scale screening hypothetical material compositions discovery.