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© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Data-driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design-to-device study that uses high-throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data-mining methods source 9431 dye candidates, which are auto-generated from the literature using a custom text-mining tool. These candidates are sifted via a data-mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co-sensitizers for dye-sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co-sensitized DSSCs with power conversion efficiencies comparable to those of the high-performance, organometallic dye, N719. These results demonstrate how data-driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.

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Journal article


Advanced energy materials

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