TY - JOUR
T1 - Materials discovery by chemical analogy
T2 - role of oxidation states in structure prediction
AU - Davies, Daniel W.
AU - Butler, Keith T.
AU - Isayev, Olexandr
AU - Walsh, Aron
N1 - Publisher Copyright:
© The Royal Society of Chemistry.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85055600521&partnerID=8YFLogxK
U2 - 10.1039/c8fd00032h
DO - 10.1039/c8fd00032h
M3 - Article
C2 - 30027179
AN - SCOPUS:85055600521
SN - 1359-6640
VL - 211
SP - 553
EP - 568
JO - Faraday Discussions
JF - Faraday Discussions
ER -