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Accelerating device characterization in perovskite solar cells via neural network approach

Author

Listed:
  • Zhao, Xinhai
  • Huang, Chaopeng
  • Birgersson, Erik
  • Suprun, Nikita
  • Tan, Hu Quee
  • Zhang, Yurou
  • Jiang, Yuxia
  • Shou, Chunhui
  • Sun, Jingsong
  • Peng, Jun
  • Xue, Hansong

Abstract

Perovskite solar cells are promising candidates for next-generation high-efficiency photovoltaic devices, especially as top cells in tandem applications. Using a physical-based optoelectronic model, we collect big data of one hundred thousand sample size to train neural network models for efficient prediction of device performance and recombination losses. Latin hypercube sampling, Bayesian regularization, and Bayesian optimization are adopted for data preparation, model training, and optimization of the neural networks, respectively. The best neural network models achieved mean squared errors below 4×10−4 on a reserved testing dataset. The computational speed of the neural network is more than one thousand times faster than traditional optoelectronic models. As a result, fast device calibration can be conducted in twenty-four seconds. The reduced computational cost allows for efficient device characterization, parametric studies, sensitivity analysis, loss analysis, and optimization. After optimizing interface recombination in our in-house fabricated devices, we observed an experimental improvement of approximately 2 % in power conversion efficiency. Additionally, we predict theoretical power conversion efficiencies of 28.9 % and 25.5 % for perovskite solar cells with band gaps of 1.56 eV and 1.63 eV, respectively.

Suggested Citation

  • Zhao, Xinhai & Huang, Chaopeng & Birgersson, Erik & Suprun, Nikita & Tan, Hu Quee & Zhang, Yurou & Jiang, Yuxia & Shou, Chunhui & Sun, Jingsong & Peng, Jun & Xue, Hansong, 2025. "Accelerating device characterization in perovskite solar cells via neural network approach," Applied Energy, Elsevier, vol. 392(C).
  • Handle: RePEc:eee:appene:v:392:y:2025:i:c:s030626192500652x
    DOI: 10.1016/j.apenergy.2025.125922
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    1. Wei Chen & Yudong Zhu & Jingwei Xiu & Guocong Chen & Haoming Liang & Shunchang Liu & Hansong Xue & Erik Birgersson & Jian Wei Ho & Xinshun Qin & Jingyang Lin & Ruijie Ma & Tao Liu & Yanling He & Alan , 2022. "Monolithic perovskite/organic tandem solar cells with 23.6% efficiency enabled by reduced voltage losses and optimized interconnecting layer," Nature Energy, Nature, vol. 7(3), pages 229-237, March.
    2. Xue, Hansong & Birgersson, Erik & Stangl, Rolf, 2019. "Correlating variability of modeling parameters with photovoltaic performance: Monte Carlo simulation of a meso-structured perovskite solar cell," Applied Energy, Elsevier, vol. 237(C), pages 131-144.
    3. Renxing Lin & Ke Xiao & Zhengyuan Qin & Qiaolei Han & Chunfeng Zhang & Mingyang Wei & Makhsud I. Saidaminov & Yuan Gao & Jun Xu & Min Xiao & Aidong Li & Jia Zhu & Edward H. Sargent & Hairen Tan, 2019. "Monolithic all-perovskite tandem solar cells with 24.8% efficiency exploiting comproportionation to suppress Sn(ii) oxidation in precursor ink," Nature Energy, Nature, vol. 4(10), pages 864-873, October.
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