机器学习辅助减反膜结构设计与界面修饰协同优化的高效稳定钙钛矿太阳能电池

doi:10.1016/j.actphy.2025.100098

Acta Phys. -Chim. Sin. ›› 2025, Vol. 41 ›› Issue (9): 100098.doi: 10.1016/j.actphy.2025.100098

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Machine learning-guided antireflection coatings architectures and interface modification for synergistically optimizing efficient and stable perovskite solar cells

Ying Liang¹^,³, Yuheng Deng¹, Shilv Yu², Jiahao Cheng¹^,⁶, Jiawei Song¹, Jun Yao⁴, Yichen Yang⁴, Wanlei Zhang¹, Wenjing Zhou¹, Xin Zhang¹, Wenjian Shen¹^,³^,*(), Guijie Liang¹^,³, Bin Li³, Yong Peng¹^,⁵, Run Hu²^,⁷^,*(), Wangnan Li¹^,³^,⁴^,*()

¹ Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
² School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei Province, China
³ Hubei Longzhong Laboratory, Wuhan University of Technology (Xiangyang Demonstration Zone), Xiangyang 441000, Hubei Province, China
⁴ Hubei Aerospace Chemical New Materials Co., Ltd., Xiangyang 441057, Hubei Province, China
⁵ State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, Hubei Province, China
⁶ State Key Laboratory of Advanced Processing and Recycling of Non-Ferrous Metals, Lanzhou University of Technology, Lanzhou 730050, Gansu Province, China
⁷ Department of Applied Physics, Kyung Hee University, Yongin-Si, Gyeonggi-do 17104, Republic of Korea

Received:2025-02-12 Revised:2025-04-03 Accepted:2025-04-28 Published:2025-07-04
Contact: Email: shenwj@hbuas.edu.cn (Wenjian Shen)hurun@hust.edu.cn (Run Hu)liwangnan@hbuas.edu.cn (Wangnan Li)
Supported by:
the National Natural Science Foundation of China(22279031); the National Natural Science Foundation of China(52422603); the Key Research and Development Plan of Hubei Province(2023BAB109); the Joint Foundation for Innovation and Development of Hubei Natural Science Foundation(2023AFD032); the Joint Foundation for Innovation and Development of Hubei Natural Science Foundation(2025AFD026); the Joint Foundation for Innovation and Development of Hubei Natural Science Foundation(2025AFD074); the Natural Science Foundation of Hubei Province(2023AFB041); the Natural Science Foundation of Hubei Province(2023AFA072); the Longzhong Talent Plan; the Graduate Quality Engineering Funding Project of Hubei University of Arts and Sciences(YZ3202304); the Independent Innovation Projects of the Hubei Longzhong Laboratory(2024KF-07); the open research fund of Suzhou Laboratory(SZLAB-1508-2024-TS016); the Interdiciplinary Research Program of HUST(5003120094)

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Abstract

Abstract:

In recent years, single-junction perovskite solar cells (PSCs) have experienced unprecedented development, approaching the Shockley-Queisser (S-Q) theoretical efficiency limit, due to versatile optimization strategies targeting functional layers to minimize energy loss. The antireflection coating (ARC), as part of the light-management strategy, plays a critical role in reducing optical loss to achieve higher efficiency. The development of multifunctional ARC that can simultaneously enhance visible light transmittance while suppressing ultraviolet (UV) light transmission, along with excellent adhesion and wear resistance on glass substrates, remains a significant challenge in current research. Herein, we propose ultra-thin ARC made of multilayer dioxides, SiO₂-TiO₂-SiO₂ (STS) films, optimized using a machine learning approach with a Bayesian optimization algorithm. This process involved parameterized modeling of multilayer dioxide ARC, physical simulations using the Transfer Matrix Method (TMM), and evaluation of antireflective performance. The optimal configuration of STS ARC consists of 100 nm SiO₂, 10 nm TiO₂, and 10 nm SiO₂, increasing the transmittance of FTO glass by 9.2% in the 400–800 nm wavelength range. The ARC effectively enhances external quantum efficiency, achieving 96.94%, thereby increasing the short-circuit current density (J_SC) and power conversion efficiency (PCE) by 4%. PSCs with STS ARC retain 81.2% of their initial efficiency after continuous UV illumination for 300 h, while control devices degrade to approximately 69%, demonstrating effective UV filtration and improved operational stability. This ARC exhibit hardness exceeding 9H on the pencil hardness scale and achieve ISO class 0/ASTM class 5B in adhesion tests, meeting the outdoor durability requirements for PSCs. In addition to optical energy loss, the accumulation of defects on the surface of the perovskite layer induces non-radiative recombination energy loss and serves as initiation sites for lattice degradation. To address this, we use 3-amidinopyridinium iodide (3-PyADI) to passivate interface defects, further improving the PCE to 24.44%. The stability of the device remains at 93% of the initial PCE after 1000 h under atmospheric conditions. The proposed ARC and PSCs structure are expected to enhance optoelectronic performance and environmental stability, providing a promising and practical path for the development of PSCs.

Key words: Machine learning, Antireflective coating, Light-management strategy, Perovskite solar cells, Interface modification

Ying Liang, Yuheng Deng, Shilv Yu, Jiahao Cheng, Jiawei Song, Jun Yao, Yichen Yang, Wanlei Zhang, Wenjing Zhou, Xin Zhang, Wenjian Shen, Guijie Liang, Bin Li, Yong Peng, Run Hu, Wangnan Li. Machine learning-guided antireflection coatings architectures and interface modification for synergistically optimizing efficient and stable perovskite solar cells[J]. Acta Phys. -Chim. Sin. 2025, 41(9), 100098. doi: 10.1016/j.actphy.2025.100098

Figures/Tables 5

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ID	Configuration (SiO₂-TiO₂-SiO₂)	FOM (Average transmittance of 400 to 800 nm)
1 (optimal)	100-10-10	0.986
2	100-135-10	0.975
3	120-20-190	0.955
4	75-25-10	0.938
5	85-10-190	0.931
6	50-135-155	0.903

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Machine learning-guided antireflection coatings architectures and interface modification for synergistically optimizing efficient and stable perovskite solar cells

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