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.
|Number of pages||9|
|Publication status||Published - 2018|
Bibliographical noteFunding Information:
DWD gratefully acknowledges support from the Engineering and Physical Sciences Research Council (EPSRC) via the Centre for Doctoral Training in Sustainable Chemical Technologies (grant no. EP/L016354/1). Calculations were carried out on the Balena HPC cluster at the University of Bath, which is maintained by Bath University Computing Services. Some of the calculations were also carried out on the UK national Archer HPC facility, accessed through membership of the UK Materials Chemistry Consortium, which is funded by EPSRC grant no. EP/L000202. JMS is supported by the EPSRC (grant no. EP/K004956/1 and EP/ P007821/1)
© 2018 The Royal Society of Chemistry.
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