# Composition based oxidation state prediction of materials using deep learning

arXiv: https://arxiv.org/abs/2211.15895 · HTML: https://mlsanity.com/paper/2211.15895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Predicting materials properties without crystal structure: Deep representation learning from stoichiometry](https://mlsanity.com/paper/1910.00617/) (2019)
- [Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials](https://mlsanity.com/paper/1911.05020/) (2019)
- [Crystal structure prediction with machine learning-based element substitution](https://mlsanity.com/paper/2201.11188/) (2022)
- [Materials Property Prediction with Uncertainty Quantification: A Benchmark Study](https://mlsanity.com/paper/2211.02235/) (2022)
- [Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials](https://mlsanity.com/paper/2204.11953/) (2022)
- [Probabilistic Generative Transformer Language models for Generative Design of Molecules](https://mlsanity.com/paper/2209.09406/) (2022)
- [Materials Transformers Language Models for Generative Materials Design: a benchmark study](https://mlsanity.com/paper/2206.13578/) (2022)

## Built on by (most cited first, 1 of 1)

- [Generative Design of inorganic compounds using deep diffusion language models](https://mlsanity.com/paper/2310.00475/) (2023)

## Abstract (arXiv)

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.