期刊
MOLECULAR INFORMATICS
卷 41, 期 1, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.202000062
关键词
aggregation; machine learning; molecular fingerprints; classification; dye sensitized solar cells
类别
资金
- Research Council of Norway (RCN) [262152]
Dye aggregation is crucial in determining the photovoltaic performance of dye sensitized solar cells. Molecular fingerprint-based methods are shown to efficiently discriminate between H- and J-aggregating dyes, achieving close to 80% classification accuracies. The prediction tools have been packaged as an R package for interested researchers.
Dye aggregation plays an important role in determining the photovoltaic performance of dye sensitized solar cells. Compared with the spectra observed in solution, it is,apriori, difficult to ascertain whether a dye is likely to show hypsochromic (H) or bathochromic (J) aggregation, until after adsorption onto the semiconductor electrode. Herein, we show that molecular fingerprint-based methods provide a fast and efficient way to discriminate between H- and J-aggregating dyes. The efficacy of the fingerprint-based classification models is demonstrated with a diverse set of over 3000 organic dyes dissolved in different solvents. Requiring only the structure of the dye and the polarity of the solvent used, the machine learning model achieves close to 80 % classification accuracies that are comparable with models based on a combination of fragment counts and topological indices. For interested researchers, we have bundled the prediction tools as an R package.
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