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Research Article

Machine learning assisted prediction of charge transfer properties in organic solar cells by using morphology-related descriptors

Lulu Fu1,3Haixia Hu2Qiang Zhu1Lifeng Zheng1Yuming Gu1Yaping Wen1Haibo Ma1Hang Yin2( )Jing Ma1,3( )
Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
School of Physics, Shandong University, Jinan 250100, China
Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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Graphical Abstract

The relationship between intermolecular packings and charge transfer state as well as charge transport properties, has been investigated by machine learning (ML), molecular dynamics (MD) simulations, density functional theory (DFT), and space-charge-limited current (SCLC) measurement. The gap between the electronic or atomic level structures of the donor/acceptor (D/A) interface and the device performance would be narrowed by using the across scale computations and ML models.

Abstract

Charge transfer and transport properties are crucial in the photophysical process of exciton dissociation and recombination at the donor/acceptor (D/A) interface. Herein, machine learning (ML) is applied to predict the charge transfer state energy (ECT) and identify the relationship between ECT and intermolecular packing structures sampled from molecular dynamics (MD) simulations on fullerene- and non-fullerene-based systems with different D/A ratios (RDA), oligomer sizes, and D/A pairs. The gradient boosting regression (GBR) exhibits satisfactory performance (r = 0.96) in predicting ECT with π-packing related features, aggregation extent, backbone of donor, and energy levels of frontier molecular orbitals. The charge transport property affected by π-packing with different RDA has also been investigated by space-charge-limited current (SCLC) measurement and MD simulations. The SCLC results indicate an improved hole transport of non-fullerene system PM6/Y6 with RDA of 1.2:1 in comparison with the 1:1 counterpart, which is mainly attributed to the bridge role of donor unit in Y6. The reduced energetic disorder is correlated with the improved miscibility of polymer with RDA increased from 1:1 to 1.2:1. The morphology-related features are also applicable to other complicated systems, such as perovskite solar cells, to bridge the gap between device performance and microscopic packing structures.

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Nano Research
Pages 3588-3596
Cite this article:
Fu L, Hu H, Zhu Q, et al. Machine learning assisted prediction of charge transfer properties in organic solar cells by using morphology-related descriptors. Nano Research, 2023, 16(2): 3588-3596. https://doi.org/10.1007/s12274-022-5000-4
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Received: 01 July 2022
Revised: 12 August 2022
Accepted: 02 September 2022
Published: 14 October 2022
© Tsinghua University Press 2022
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