Quantitative Structure-Property Relationship Modeling of Ruthenium Sensitizers for Solar Cells Applications: Novel Tools for Designing Promising Candidates.

Sara Tortorella^a, Gabriele Cruciani^a, Filippo De Angelis^b, Gabriele Marotta^b

^aDrug Design and Molecular Modeling Laboratory, Department of Chemistry, Biology and Biotechnology, University of Perugia

^bComputationalLaboratory for Hybrid/Organic Photovoltaics (CLHYO), Institute of Molecular Science and Technologies (ISTM-CNR), Via Elce di Sotto 8, Perugia

International Conference on Hybrid and Organic Photovoltaics
Proceedings of International Conference on Hybrid and Organic Photovoltaics 2015 (HOPV15)

Roma, Italy, 2015 May 11th - 13th

Organizer: Filippo De Angelis

Poster, Sara Tortorella, 449
Publication date: 5th February 2015

To date, the most diffused way of screening new potential sensitizers for dye sensitized solar cells is via the traditional time and money consuming trial and error approach.1 In this study we explore the possibility of extending drug discovery and cheminformatic approaches2,3 to the field of material science with the aim of a quantitative structure-property relationship elucidation that could lead to a fast and inexpensive in silico screening of new ruthenium sensitizers for third generation solar cells. Starting from the building of a database of already tested candidates used to train the predictive models, appropriate descriptors extracted from images of 3D molecular interaction fields (GRID/MIFs, Fig. 1)4, as well as semi-empirical calculated descriptors, were chosen to describe the target structures. Then, structure-performance (Jsc, Voc and PCE) models were built and analysed in order to elucidate structure-property relationships and interesting results were obtained. In particular, we were able to find the molecular descriptors that more contribute to enhance the performance investigated, thus finding directives for the design of potentially high-performing candidates. We also proposed an efficient correction of the experimental Jsc and Voc based on the quantity of the LiI additive for electrolyte used to build the devices. In the early stage of this project, we demonstrated that molecular modelling methods could be successfully extended to the field of material science as alternative to the traditional expensive and time-consuming trial and error approach.
Fig. 1Example of a) shape and b) hydrophobic (red) andhydrophilic (blue) molecularinteractionfields (MIFs) for a ligandobject of study.
[1] J.-F. Yin, M. Velayudham, D. Bhattacharya, H.-C. Lin and K.-L. Lu, Coord. Chem. Rev., 2012, 256, 3008–3035. [2] T. Le, V. C. Epa, F. R. Burden and D. a Winkler, Chem. Rev., 2012, 112, 2889–2919. [3]P. Crivori, G. Cruciani, P. a Carrupt and B. Testa, J. Med. Chem., 2000, 43, 2204–2216. [4] G. Cruciani, P. Crivori, P.-A. Carrupt and B. Testa, J. Mol. Struc.: THEOCHEM, 2000, 503, 17-30.

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