Authors
Cooper, Christopher B; Beard, Edward J; Vazquez-Mayagoitia, Alvaro; Stan, Liliana; Stenning, Gavin B. G.; Nye, Daniel W; Vigil, Julian A; Tomar, Tina; Jia, Jingwen; Bodedla, Govardhana Babu; Chen, Song; Aibar, Lucia Gallego; Ontaneda, Santiago Franco; Carella, Antonio; Thomas, K.R. Justin; Xue, Song; Zhu, Xunjin; Cole, Jacqueline
Abstract
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.