Issue 4, 2018

Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure

Abstract

The standard paradigm in computational materials science is INPUT: STRUCTURE; OUTPUT: PROPERTIES, which has yielded many successes but is ill-suited for exploring large areas of chemical and configurational hyperspace. We report a high-throughput screening procedure that uses compositional descriptors to search for new photoactive semiconducting compounds. We show how feeding high-ranking element combinations to structure prediction algorithms can constitute a pragmatic computer-aided materials design approach. Techniques based on structural analogy (data mining of known lattice types) and global searches (direct optimisation using evolutionary algorithms) are combined for translating between chemical composition and crystal structure. The properties of four novel chalcohalides (Sn5S4Cl2, Sn4SF6, Cd5S4Cl2 and Cd4SF6) are predicted, of which two are calculated to have bandgaps in the visible range of the electromagnetic spectrum.

Graphical abstract: Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure

Supplementary files

Article information

Article type
Edge Article
Submitted
09 9 2017
Accepted
04 12 2017
First published
04 12 2017
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2018,9, 1022-1030

Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure

Daniel W. Davies, K. T. Butler, J. M. Skelton, C. Xie, A. R. Oganov and A. Walsh, Chem. Sci., 2018, 9, 1022 DOI: 10.1039/C7SC03961A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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