Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Nano Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zheng, Z.; Awartani, O. M.; Gautam, B.; Liu, D. L.; Qin, Y. P.; Li, W. N.; Bataller, A.; Gundogdu, K.; Ade, H.; Hou, J. H. Efficient charge transfer and fine-tuned energy level alignment in a THF-processed fullerene-free organic solar cell with 11.3% efficiency. Adv. Mater. 2017, 29, 1604241.

    Article  Google Scholar 

  2. Hou, J. H.; Inganäs, O.; Friend, R. H.; Gao, F. Organic solar cells based on non-fullerene acceptors. Nat. Mater. 2018, 17, 119–128.

    Article  CAS  Google Scholar 

  3. Yu, G.; Gao, J.; Hummelen, J. C.; Wudl, F.; Heeger, A. J. Polymer photovoltaic cells: Enhanced efficiencies via a network of internal donor-acceptor heterojunctions. Science 1995, 270, 1789–1791.

    Article  CAS  Google Scholar 

  4. Park, S.; Kim, T.; Yoon, S.; Koh, C. W.; Woo, H. Y.; Son, H. J. Progress in materials, solution processes, and long-term stability for large-area organic photovoltaics. Adv. Mater. 2020, 32, 2002217.

    Article  CAS  Google Scholar 

  5. Qiu, Z.; Hammer, B. A. G.; Müllen, K. Conjugated polymers—Problems and promises. Prog. Polym. Sci. 2020, 100, 101179.

    Article  CAS  Google Scholar 

  6. Lee, C.; Lee, S.; Kim, G. U.; Lee, W.; Kim, B. J. Recent advances, design guidelines, and prospects of all-polymer solar cells. Chem. Rev. 2019, 119, 8028–8086.

    Article  CAS  Google Scholar 

  7. Dang, M. T.; Hirsch, L.; Wantz, G. P3HT:PCBM, best seller in polymer photovoltaic research. Adv. Mater. 2011, 23, 3597–3602.

    Article  CAS  Google Scholar 

  8. Caddeo, C.; Filippetti, A.; Bosin, A.; Videlot-Ackermann, C.; Ackermann, J.; Mattoni, A. Theoretical insight on PTB7:PC71BM, PTB7-th:PC71BM and Si-PCPDTBT:PC71BM interactions governing blend nanoscale morphology for efficient solar cells. Nano Energy 2021, 82, 105708.

    Article  CAS  Google Scholar 

  9. Lin, Y. Z.; Wang, J. Y.; Zhang, Z. G.; Bai, H. T.; Li, Y. F.; Zhu, D. B.; Zhan, X. W. An electron acceptor challenging fullerenes for efficient polymer solar cells. Adv. Mater. 2015, 27, 1170–1174.

    Article  CAS  Google Scholar 

  10. Yuan, J.; Zhang, Y. Q.; Zhou, L. Y.; Zhang, G. C.; Yip, H. L.; Lau, T. K.; Lu, X. H.; Zhu, C.; Peng, H. J.; Johnson, P. A. et al. Single-junction organic solar cell with over 15% efficiency using fused-ring acceptor with electron-deficient core. Joule 2019, 3, 1140–1151.

    Article  CAS  Google Scholar 

  11. Sahu, H.; Yang, F.; Ye, X. B.; Ma, J.; Fang, W. H.; Ma, H. B. Designing promising molecules for organic solar cells via machine learning assisted virtual screening. J. Mater. Chem. A 2019, 7, 17480–17488.

    Article  CAS  Google Scholar 

  12. Wen, Y. P.; Fu, L. L.; Li, G. Q.; Ma, J.; Ma, H. B. Accelerated discovery of potential organic dyes for dye-sensitized solar cells by interpretable machine learning models and virtual screening. Sol. RRL 2020, 4, 2000110.

    Article  CAS  Google Scholar 

  13. Zhang, Q.; Zheng, Y. J.; Sun, W. B.; Ou, Z. P.; Odunmbaku, O.; Li, M.; Chen, S. S.; Zhou, Y. L.; Li, J.; Qin, B. et al. High-efficiency non-fullerene acceptors developed by machine learning and quantum chemistry. Adv. Sci. (Weinh.) 2022, 9, 2104742.

    CAS  Google Scholar 

  14. Lee, M. H. Insights from machine learning techniques for predicting the efficiency of fullerene derivatives-based ternary organic solar cells at ternary blend design. Adv. Energy Mater. 2019, 9, 1900891.

    Article  Google Scholar 

  15. Zhao, Z. W.; del Cueto, M.; Geng, Y.; Troisi, A. Effect of increasing the descriptor set on machine learning prediction of small molecule-based organic solar cells. Chem. Mater. 2020, 32, 7777–7787.

    Article  CAS  Google Scholar 

  16. Sun, W. B.; Zheng, Y. J.; Yang, K.; Zhang, Q.; Shah, A. A.; Wu, Z.; Sun, Y. Y.; Feng, L.; Chen, D. Y.; Xiao, Z. Y. et al. Machine learning-assisted molecular design and efficiency prediction for highperformance organic photovoltaic materials. Sci. Adv. 2019, 5, eaay4275.

    Article  CAS  Google Scholar 

  17. Nagasawa, S.; Al-Naamani, E.; Saeki, A. Computer-aided screening of conjugated polymers for organic solar cell: Classification by random forest. J. Phys. Chem. Lett. 2018, 9, 2639–2646.

    Article  CAS  Google Scholar 

  18. Kranthiraja, K.; Saeki, A. Experiment-oriented machine learning of polymer: Non-fullerene organic solar cells. Adv. Funct. Mater. 2021, 31, 2011168.

    Article  CAS  Google Scholar 

  19. Padula, D.; Troisi, A. Concurrent optimization of organic donor-acceptor pairs through machine learning. Adv. Energy Mater. 2019, 9, 1902463.

    Article  CAS  Google Scholar 

  20. Lee, M. H. A machine learning-based design rule for improved open-circuit voltage in ternary organic solar cells. Adv. Intell. Syst. 2020, 2, 1900108.

    Article  Google Scholar 

  21. Padula, D.; Simpson, J. D.; Troisi, A. Combining electronic and structural features in machine learning models to predict organic solar cells properties. Mater. Horiz. 2019, 6, 343–349.

    Article  CAS  Google Scholar 

  22. Sahu, H.; Ma, H. B. Unraveling correlations between molecular properties and device parameters of organic solar cells using machine learning. J. Phys. Chem. Lett. 2019, 10, 7277–7284.

    Article  CAS  Google Scholar 

  23. Rodríguez-Martínez, X.; Pascual-San-José, E.; Fei, Z. P.; Heeney, M.; Guimerà, R.; Campoy-Quiles, M. Predicting the photocurrent-composition dependence in organic solar cells. Energy Environ. Sci. 2021, 14, 986–994.

    Article  Google Scholar 

  24. Deibel, C.; Strobel, T.; Dyakonov, V. Role of the charge transfer state in organic donor-acceptor solar cells. Adv. Mater. 2010, 22, 4097–4111.

    Article  CAS  Google Scholar 

  25. Vandewal, K. Interfacial charge transfer states in condensed phase systems. Annu. Rev. Phys. Chem. 2016, 67, 113–133.

    Article  CAS  Google Scholar 

  26. Lin, Y. L.; Fusella, M. A.; Rand, B. P. The impact of local morphology on organic donor/acceptor charge transfer states. Adv. Energy Mater. 2018, 8, 1702816.

    Article  Google Scholar 

  27. Gao, F.; Inganäs, O. Charge generation in polymer-fullerene bulk-heterojunction solar cells. Phys. Chem. Chem. Phys. 2014, 16, 20291–20304.

    Article  CAS  Google Scholar 

  28. Rinderle, M.; Kaiser, W.; Mattoni, A.; Gagliardi, A. Machine-learned charge transfer integrals for multiscale simulations in organic thin films. J. Phys. Chem. C 2020, 124, 17733–17743.

    Article  CAS  Google Scholar 

  29. Brian, D.; Sun, X. Charge-transfer landscape manifesting the structure-rate relationship in the condensed phase via machine learning. J. Phys. Chem. B 2021, 125, 13267–13278.

    Article  CAS  Google Scholar 

  30. Coropceanu, V.; Chen, X. K.; Wang, T. H.; Zheng, Z. L.; Brédas, J. L. Charge-transfer electronic states in organic solar cells. Nat. Rev. Mater. 2019, 4, 689–707.

    Article  Google Scholar 

  31. Rao, A.; Chow, P. C. Y.; Gélinas, S.; Schlenker, C. W.; Li, C. Z.; Yip, H. L.; Jen, A. K. Y.; Ginger, D. S.; Friend, R. H. The role of spin in the kinetic control of recombination in organic photovoltaics. Nature 2013, 500, 435–439.

    Article  CAS  Google Scholar 

  32. Mishra, A.; Bäuerle, P. Small molecule organic semiconductors on the move: Promises for future solar energy technology. Angew. Chem., Int. Ed. 2012, 51, 2020–2067.

    Article  CAS  Google Scholar 

  33. Zhu, L.; Zhang, M.; Zhou, G. Q.; Hao, T. Y.; Xu, J. Q.; Wang, J.; Qiu, C. Q.; Prine, N.; Ali, J.; Feng, W. et al. Efficient organic solar cell with 16.88% efficiency enabled by refined acceptor crystallization and morphology with improved charge transfer and transport properties. Adv. Energy Mater. 2020, 70, 1904234.

    Article  Google Scholar 

  34. Wang, T. H.; Kupgan, G.; Brédas, J. L. Organic photovoltaics: Relating chemical structure, local morphology, and electronic properties. Trends Chem. 2020, 2, 535–554.

    Article  CAS  Google Scholar 

  35. Liang, Y. Y.; Xu, Z.; Xia, J. B.; Tsai, S. T.; Wu, Y.; Li, G.; Ray, C.; Yu, L. P. For the bright future—Bulk heterojunction polymer solar cells with power conversion efficiency of 7.4%. Adv. Mater. 2010, 22, E135–E138.

    Article  CAS  Google Scholar 

  36. Lou, S. J.; Szarko, J. M.; Xu, T.; Yu, L. P.; Marks, T. J.; Chen, L. X. Effects of additives on the morphology of solution phase aggregates formed by active layer components of high-efficiency organic solar cells. J. Am. Chem. Soc. 2011, 133, 20661–20663.

    Article  CAS  Google Scholar 

  37. Zhu, W. G.; Spencer, A. P.; Mukherjee, S.; Alzola, J. M.; Sangwan, V. K.; Amsterdam, S. H.; Swick, S. M.; Jones, L. O.; Heiber, M. C.; Herzing, A. A. et al. Crystallography, morphology, electronic structure, and transport in non-fullerene/non-indacenodithienothiophene polymer: Y6 solar cells. J. Am. Chem. Soc. 2020, 142, 14532–14547.

    Article  CAS  Google Scholar 

  38. Li, M. Y.; Pan, Y. Q.; Sun, G. Y.; Geng, Y. Charge transfer mechanisms regulated by the third component in ternary organic solar cells. J. Phys. Chem. Lett. 2021, 12, 8982–8990.

    Article  CAS  Google Scholar 

  39. Pan, Q. Q.; Li, S. B.; Duan, Y. C.; Wu, Y.; Zhang, J.; Geng, Y.; Zhao, L.; Su, Z. M. Exploring what prompts ITIC to become a superior acceptor in organic solar cell by combining molecular dynamics simulation with quantum chemistry calculation. Phys. Chem. Chem. Phys. 2017, 19, 31227–31235.

    Article  CAS  Google Scholar 

  40. Bai, R. R.; Zhang, C. R.; Liu, Z. J.; Chen, X. K.; Wu, Y. Z.; Wang, W.; Chen, H. S. Electric field effects on organic photovoltaic heterojunction interfaces: The model case of pentacene/C60. Comput. Theor. Chem. 2020, 1186, 112914.

    Article  CAS  Google Scholar 

  41. Liu, C.; Wang, K.; Gong, X.; Heeger, A. J. Low bandgap semiconducting polymers for polymeric photovoltaics. Chem. Soc. Rev. 2016, 45, 4825–4846.

    Article  CAS  Google Scholar 

  42. Wang, T. H.; Brédas, J. L. Organic photovoltaics: Understanding the preaggregation of polymer donors in solution and its morphological impact. J. Am. Chem. Soc. 2021, 143, 1822–1835.

    Article  CAS  Google Scholar 

  43. Wang, T. H.; Brédas, J. L. Organic solar cells based on non-fullerene small-molecule acceptors: Impact of substituent position. Matter 2020, 2, 119–135.

    Article  CAS  Google Scholar 

  44. Liu, Y.; Xian, K. H.; Peng, Z. X.; Gao, M. Y.; Shi, Y. B.; Deng, Y. F.; Geng, Y. H.; Ye, L. Tuning the molar mass of P3HT via direct arylation polycondensation yields optimal interaction and high efficiency in nonfullerene organic solar cells. J. Mater. Chem. A 2021, 9, 19874–19885.

    Article  CAS  Google Scholar 

  45. Lv, J.; Tang, H.; Huang, J. M.; Yan, C. Q.; Liu, K.; Yang, Q. G.; Hu, D. Q.; Singh, R.; Lee, J.; Lu, S. R. et al. Additive-induced miscibility regulation and hierarchical morphology enable 17.5% binary organic solar cells. Energy Environ. Sci. 2021, 74, 3044–3052.

    Article  Google Scholar 

  46. Zhou, N. J.; Dudnik, A. S.; Li, T. I. N. G.; Manley, E. F.; Aldrich, T. J.; Guo, P. J.; Liao, H. C.; Chen, Z. H.; Chen, L. X.; Chang, R. P. H. et al. All-polymer solar cell performance optimized via systematic molecular weight tuning of both donor and acceptor polymers. J. Am. Chem. Soc. 2016, 138, 1240–1251.

    Article  CAS  Google Scholar 

  47. Zhang, L.; Huang, X. L.; Duan, C. H.; Peng, Z. X.; Ye, L.; Kirby, N.; Huang, F.; Cao, Y. Morphology evolution with polymer chain propagation and its impacts on device performance and stability of non-fullerene solar cells. J. Mater. Chem. A 2021, 9, 556–565.

    Article  CAS  Google Scholar 

  48. Liu, F.; Chen, D.; Wang, C.; Luo, K. Y.; Gu, W. Y.; Briseno, A. L.; Hsu, J. W. P.; Russell, T. P. Molecular weight dependence of the morphology in P3HT: PCBM solar cells. ACS Appl. Mater. Interfaces 2014, 6, 19876–19887.

    Article  CAS  Google Scholar 

  49. Bhalla, D. Ensemble Learning: Boosting and Bagging [Online]. 2015. https://www.listendata.com/2015/03/ensemble-learning-boosting-and-bagging.html (aaccessed July 16, 2022).

  50. Priyadarshi, R.; Panigrahi, A.; Routroy, S.; Garg, G. K. Demand forecasting at retail stage for selected vegetables: A performance analysis. J. Modell. Manage. 2019, 74, 1042–1063.

    Article  Google Scholar 

  51. Graham, K. R.; Cabanetos, C.; Jahnke, J. P.; Idso, M. N.; El Labban, A.; Ngongang Ndjawa, G. O.; Heumueller, T.; Vandewal, K.; Salleo, A.; Chmelka, B. F. et al. Importance of the donor: Fullerene intermolecular arrangement for high-efficiency organic photovoltaics. J. Am. Chem. Soc. 2014, 136, 9608–9618.

    Article  CAS  Google Scholar 

  52. Yang, B.; Yi, Y. P.; Zhang, C. R.; Aziz, S. G.; Coropceanu, V.; Brédas, J. L. Impact of electron delocalization on the nature of the charge-transfer states in model pentacene/C60 interfaces: A density functional theory study. J. Phys. Chem. C 2014, 118, 27648–27656.

    Article  CAS  Google Scholar 

  53. Perdigón-Toro, L.; Zhang, H. T.; Markina, A.; Yuan, J.; Hosseini, S. M.; Wolff, C. M.; Zuo, G. Z.; Stolterfoht, M.; Zou, Y. P.; Gao, F. et al. Barrierless free charge generation in the high-performance PM6: Y6 bulk heterojunction non-fullerene solar cell. Adv. Mater. 2020, 32, 1906763.

    Article  Google Scholar 

  54. Hu, H. X.; Fu, L. L.; Zhang, K. N.; Gao, K.; Ma, J.; Hao, X. T.; Yin, H. Observing halogen-bond-assisted electron transport in highperformance polymer solar cells. Appl. Phys. Lett. 2021, 119, 183302.

    Article  CAS  Google Scholar 

  55. Li, N.; Perea, J. D.; Kassar, T.; Richter, M.; Heumueller, T.; Matt, G. J.; Hou, Y.; Güldal, N. S.; Chen, H. W.; Chen, S. et al. Abnormal strong burn-in degradation of highly efficient polymer solar cells caused by spinodal donor-acceptor demixing. Nat. Commun. 2017, 8, 14541.

    Article  CAS  Google Scholar 

  56. Gasperini, A.; Sivula, K. Effects of molecular weight on microstructure and carrier transport in a semicrystalline poly(thieno)thiophene. Macromolecules 2013, 46, 9349–9358.

    Article  CAS  Google Scholar 

  57. Yao, H. F.; Cui, Y.; Qian, D. P.; Ponseca, C. S. Jr.; Honarfar, A.; Xu, Y.; Xin, J. M.; Chen, Z. Y.; Hong, L.; Gao, B. W. et al. 14.7% efficiency organic photovoltaic cells enabled by active materials with a large electrostatic potential difference. J. Am. Chem. Soc. 2019, 141, 7743–7750.

    Article  CAS  Google Scholar 

  58. Xu, Y.; Yao, H. F.; Ma, L. J.; Hong, L.; Li, J. Y.; Liao, Q.; Zu, Y. F.; Wang, J. W.; Gao, M. Y.; Ye, L. et al. Tuning the hybridization of local exciton and charge-transfer states in highly efficient organic photovoltaic cells. Angew. Chem., Int. Ed. 2020, 59, 9004–9010.

    Article  CAS  Google Scholar 

  59. Wei, Q. Y.; Yuan, J.; Yi, Y. P.; Zhang, C. F.; Zou, Y. P. Y6 and its derivatives: Molecular design and physical mechanism. Natl. Sci. Rev. 2021, 8, nwab121.

    Article  CAS  Google Scholar 

  60. Han, G. C.; Guo, Y.; Ning, L.; Yi, Y. P. Improving the electron mobility of ITIC by end-group modulation: The role of fluorination and π-extension. Sol. RRL 2019, 3, 1800251.

    Article  Google Scholar 

  61. Han, G. C.; Guo, Y.; Song, X. X.; Wang, Y.; Yi, Y. P. Terminal π-π stacking determines three-dimensional molecular packing and isotropic charge transport in an A—π—A electron acceptor for non-fullerene organic solar cells. J. Mater. Chem. C 2017, 5, 4852–4857.

    Article  CAS  Google Scholar 

  62. Ho, C. H. Y.; Cheung, S. H.; Li, H. W.; Chiu, K. L.; Cheng, Y. H.; Yin, H.; Chan, M. H.; So, F.; Tsang, S. W.; So, S. K. Using ultralow dosages of electron acceptor to reveal the early stage donor-acceptor electronic interactions in bulk heterojunction blends. Adv. Energy Mater. 2017, 7, 1602360.

    Article  Google Scholar 

  63. Zhang, T.; Nakajima, T.; Cao, H. H.; Sun, Q.; Ban, H. X.; Pan, H.; Yu, H. X.; Zhang, Z. G.; Zhang, X. L.; Shen, Y. et al. Controlling quantum-well width distribution and crystal orientation in two-dimensional tin halide perovskites via a strong interlayer electrostatic interaction. ACS Appl. Mater. Interfaces 2021, 13, 49907–49915.

    Article  CAS  Google Scholar 

  64. Li, H. Y.; Song, J. M.; Pan, W. T.; Xu, D. R.; Zhu, W. A.; Wei, H. T.; Yang, B. Sensitive and stable 2D perovskite single-crystal X-ray detectors enabled by a supramolecular anchor. Adv. Mater. 2020, 32, 2003790.

    Article  CAS  Google Scholar 

  65. Chirvony, V. S.; Suárez, I.; Rodríguez-Romero, J.; Vázquez-Cárdenas, R.; Sanchez-Diaz, J.; Molina-Sánchez, A.; Barea, E. M.; Mora-Seró, I.; Martínez-Pastor, J. P. Inhomogeneous broadening of photoluminescence spectra and kinetics of nanometer-thick (phenethylammonium)2PbI4 perovskite thin films: Implications for optoelectronics. ACS Appl. Nano Mater. 2021, 4, 6170–6177.

    Article  CAS  Google Scholar 

  66. Wang, P. X.; Najarian, A. M.; Hao, Z. M.; Johnston, A.; Voznyy, O.; Hoogland, S.; Sargent, E. H. Structural distortion and bandgap increase of two-dimensional perovskites induced by trifluoromethyl substitution on spacer cations. J. Phys. Chem. Lett. 2020, 11, 10144–10149.

    Article  CAS  Google Scholar 

  67. Cortecchia, D.; Mróz, W.; Neutzner, S.; Borzda, T.; Folpini, G.; Brescia, R.; Petrozza, A. Defect engineering in 2D perovskite by Mn(II) doping for light-emitting applications. Chem 2019, 5, 2146–2158.

    Article  CAS  Google Scholar 

  68. Li, Y. Z.; Ji, C. M.; Li, L. N.; Wang, S. S.; Han, S. G.; Peng, Y.; Zhang, S. H.; Luo, J. H. (γ-Methoxy propyl amine)2PbBr4: A novel two-dimensional halide hybrid perovskite with efficient bluish white-light emission. Inorg. Chem. Front. 2021, 8, 2119–2124.

    Article  CAS  Google Scholar 

  69. Salomon-Ferrer, R.; Case, D. A.; Walker, R. C. An overview of the Amber biomolecular simulation package. WIREs Comput. Mol. Sci. 2013, 3, 198–210.

    Article  CAS  Google Scholar 

  70. Götz, A. W.; Williamson, M. J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J. Chem. Theory Comput. 2012, 8, 1542–1555.

    Article  Google Scholar 

  71. Salomon-Ferrer, R.; Götz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput. 2013, 9, 3878–3888.

    Article  CAS  Google Scholar 

  72. Wang, J. M.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157–1174.

    Article  CAS  Google Scholar 

  73. Hwang, M. J.; Stockfisch, T. P.; Hagler, A. T. Derivation of class II force fields. 2. Derivation and characterization of a class II force field, CFF93, for the alkyl functional group and alkane molecules. J. Am. Chem. Soc. 1994, 116, 2515–2525.

    Article  CAS  Google Scholar 

  74. Sun, H. Ab initio calculations and force field development for computer simulation of polysilanes. Macromolecules 1995, 28, 701–712.

    Article  CAS  Google Scholar 

  75. Sun, H.; Mumby, S. J.; Maple, J. R.; Hagler, A. T. Ab initio calculations on small molecule analogs of polycarbonates. J. Phys. Chem. 1995, 99, 5873–5882.

    Article  CAS  Google Scholar 

  76. Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 2010, 132, 154104.

    Article  Google Scholar 

  77. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H. et al. Gaussian 16; Gaussian, Inc.: Wallingford, 2016.

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China

    Lulu Fu, Qiang Zhu, Lifeng Zheng, Yuming Gu, Yaping Wen, Haibo Ma & Jing Ma

  2. School of Physics, Shandong University, Jinan, 250100, China

    Haixia Hu & Hang Yin

  3. Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China

    Lulu Fu & Jing Ma

Authors
  1. Lulu Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haixia Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lifeng Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuming Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yaping Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haibo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hang Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jing Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hang Yin or Jing Ma.

Electronic Supplementary Material

12274_2022_5000_MOESM1_ESM.pdf

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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 Res. 16, 3588–3596 (2023). https://doi.org/10.1007/s12274-022-5000-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12274-022-5000-4

Keywords

Search

Navigation