Materials discovery by chemical analogy: role of oxidation states in structure prediction

Daniel W. Davies, Keith T. Butler, Olexandr Isayev, Aron Walsh

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


The likelihood of an element to adopt a specific oxidation state in a solid, given a certain set of neighbours, might often be obvious to a trained chemist. However, encoding this information for use in high-throughput searches presents a significant challenge. We carry out a statistical analysis of the occurrence of oxidation states in 16 735 ordered, inorganic compounds and show that a large number of cations are only likely to exhibit certain oxidation states in combination with particular anions. We use this data to build a model that ascribes probabilities to the formation of hypothetical compounds, given the proposed oxidation states of their constituent species. The model is then used as part of a high-throughput materials design process, which significantly narrows down the vast compositional search space for new ternary metal halide compounds. Finally, we employ a machine learning analysis of existing compounds to suggest likely structures for a small subset of the candidate compositions. We predict two new compounds, MnZnBr4 and YSnF7, that are thermodynamically stable according to density functional theory, as well as four compounds, MnCdBr4, MnRu2Br8, ScZnF5 and ZnCoBr4, which lie within the window of metastability.

Original languageEnglish
Pages (from-to)553-568
Number of pages16
JournalFaraday Discussions
Publication statusPublished - 2018

Bibliographical note

Publisher Copyright:
© The Royal Society of Chemistry.

All Science Journal Classification (ASJC) codes

  • Physical and Theoretical Chemistry


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