Identifying structure–absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics

Autor: Jun Yan, Xabier Rodríguez-Martínez, Drew Pearce, Hana Douglas, Danai Bili, Mohammed Azzouzi, Flurin Eisner, Alise Virbule, Elham Rezasoltani, Valentina Belova, Bernhard Dörling, Sheridan Few, Anna A. Szumska, Xueyan Hou, Guichuan Zhang, Hin-Lap Yip, Mariano Campoy-Quiles, Jenny Nelson
Přispěvatelé: European Research Council, Ministerio de Ciencia, Innovación y Universidades (España), European Cooperation in Science and Technology, Guangdong Science and Technology Department, Commission of the European Communities
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Digital.CSIC. Repositorio Institucional del CSIC
instname
Energy & Environmental Science
Popis: Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of their high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ∼500 π-conjugated molecules. The calculations are first validated by comparison with experimental measurements in solution and solid state using common fullerene and non-fullerene acceptors. We find that the molar extinction coefficient (εd,max) shows reasonable agreement between calculation in vacuum and experiment for molecules in solution, highlighting the effectiveness of TDDFT for predicting optical properties of organic π-conjugated molecules. We then perform a statistical analysis based on molecular descriptors to identify which features are important in defining the absorption strength. This allows us to identify structural features that are correlated with high absorption strength in NFAs and could be used to guide molecular design: highly absorbing NFAs should possess a planar, linear, and fully conjugated molecular backbone with highly polarisable heteroatoms. We then exploit a random decision forest algorithm to draw predictions for εd,max using a computational framework based on extended tight-binding Hamiltonians, which shows reasonable predicting accuracy with lower computational cost than TDDFT. This work provides a general understanding of the relationship between molecular structure and absorption strength in π-conjugated organic molecules, including NFAs, while introducing predictive machine-learning models of low computational cost.
J. N., J. Y., D. P., M. A., F. E., and E. R. thank the European Research Council for support under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 742708 and 648901). The authors at ICMAB acknowledge financial support from the Spanish Ministry of Science and Innovation through the Severo Ochoa” Program for Centers of Excellence in R&D (No. CEX2019-000917-S), and project PGC2018-095411-B-I00. E. R. is grateful to the Fonds de Recherche du Quebec-Nature et technologies (FRQNT) for a postdoctoral fellowship and acknowledges financial support from the European Cooperation in Science and Technology. M. A. thanks the Engineering and Physical Sciences Research Council (EPSRC) for support via doctoral studentships. F. E. thanks the Engineering and Physical Sciences Research Council (EPSRC) for support via the Post-Doctoral Prize Fellowship. X. R.-M. acknowledges Prof. Olle Inganäs and the Knut and Allice Wallenberg Foundation for funding of his current postdoctoral position. H.-L. Yip thanks the support from Guangdong Major Project of Basic and Applied Basic Research (2019B030302007). The TOC figure and Fig. 5a in the manuscript include freely available resources from Flaticon.com. J. Y. thank Xiaodan Ge for her support.
With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S).
Databáze: OpenAIRE
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