Composition based oxidation state prediction of materials using deep learning
This work addresses a key bottleneck in materials science by enabling oxidation state prediction from composition alone, which is incremental but important for accelerating material discovery.
The paper tackled the problem of predicting oxidation states of inorganic compounds using only chemical composition, which is crucial for new material discovery when structures are unavailable, and achieved 96.82% accuracy on a benchmark dataset and 97.61% for oxide materials.
Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have 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 is more important in new material discovery for which the structures are not even available. This work proposes a novel deep learning based BERT transformer language model BERTOS for predicting the oxidation states of all elements of inorganic compounds given only their chemical composition. Our 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. We also demonstrate how it can be used to conduct large-scale screening of hypothetical material compositions for materials discovery.