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Nano Research 2023, 16(2): 3588-3596 https://doi.org/10.1007/s12274-022-5000-4
Research Article | Issue | Published: 14 October 2022

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

Show Author's Information Lulu Fu1,3,  Haixia Hu2,  Qiang Zhu1,  Lifeng Zheng1,  Yuming Gu1,  Yaping Wen1,  Haibo Ma1,  Hang 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
Keywords:
machine learning, charge transfer, charge transport, organic solar cells, packing modes
Topics:
Electronic states
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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Abstract Full text Electronic supplementary material About this article

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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Electronic supplementary material
About this article

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

Show Author's information Lulu Fu1,3, Haixia Hu2, Qiang Zhu1, Lifeng Zheng1, Yuming Gu1, Yaping Wen1, Haibo Ma1, Hang 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

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.

Keywords: machine learning, charge transfer, charge transport, organic solar cells, packing modes

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Acknowledgements

Publication history

Received: 01 July 2022
Revised: 12 August 2022
Accepted: 02 September 2022
Published: 14 October 2022
Issue date: February 2023

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 22033004 and 21873045). We are grateful to the High Performance Computing Centre of Nanjing University for providing the IBM Blade cluster system.

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