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.
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Davies, D., Butler, K., Skelton, J., Xie, C., Oganov, A., & Walsh, A. (2018). Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure. Chemical Science, 9(4), 1022 - 1030. https://doi.org/10.1039/C7SC03961A