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

物理化学学报 >> 2025, Vol. 41 >> Issue (9): 100098.doi: 10.1016/j.actphy.2025.100098

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

梁英¹^,³^,†, 邓羽恒¹^,†, 余士律²^,†, 程家豪¹^,⁶, 宋嘉伟¹, 姚俊⁴, 杨亦辰⁴, 张万雷¹, 周文靖¹, 张欣¹, 沈文剑¹^,³^,*(), 梁桂杰¹^,³, 李彬³, 彭勇¹^,⁵, 胡润²^,⁷^,*(), 李望南¹^,³^,⁴^,*()

¹ 湖北文理学院, 低维光电材料与器件湖北省重点实验室, 湖北襄阳 441053
² 华中科技大学, 能源与动力工程学院, 湖北武汉 430074
³ 武汉理工大学(襄阳示范区), 湖北隆中实验室, 湖北襄阳 441000
⁴ 湖北航天化学新材料科技有限公司, 湖北襄阳 441057
⁵ 武汉理工大学, 材料复合新技术国家重点实验室, 湖北武汉 430070
⁶ 兰州理工大学, 省部共建有色金属先进加工与再利用国家重点实验室, 甘肃兰州 730050
⁷ Department of Applied Physics, Kyung Hee University, Yongin-Si, Gyeonggi-do 17104, Republic of Korea

收稿日期:2025-02-12 修回日期:2025-04-03 录用日期:2025-04-28 发布日期:2025-07-04
通讯作者: Email: shenwj@hbuas.edu.cn (沈文剑)hurun@hust.edu.cn (胡润)liwangnan@hbuas.edu.cn (李望南)
作者简介:
^† These authors contribute equally to this work.
基金资助:
国家自然科学基金(22279031); 国家自然科学基金(52422603); 湖北省重点研发计划(2023BAB109); 湖北省自然科学基金创新发展联合基金(2023AFD032); 湖北省自然科学基金创新发展联合基金(2025AFD026); 湖北省自然科学基金创新发展联合基金(2025AFD074); 湖北省自然科学基金(2023AFB041); 湖北省自然科学基金(2023AFA072); 襄阳市隆中人才计划; 湖北文理学院研究生质量工程建设项目(YZ3202304); 湖北隆中实验室自主创新课题(2024KF-07); 苏州实验室开放基金(SZLAB-1508-2024-TS016); 华中科技大学交叉研究支持计划项目(5003120094)

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)

1. ACTPHY-2025.100098-SI-7p.pdf(6995KB)

摘要/Abstract

摘要：

近年来，单节钙钛矿太阳电池(PSCs)通过对功能层进行多元优化策略最小化能量损失使器件效率迅速提升，逐渐逼近肖克利-奎伊瑟(S-Q)理论效率极限。作为光管理策略的重要组成部分，减反射涂层(ARC)在降低光学能量损耗实现高效率方面发挥着关键作用。开发出具有多功能的ARC，能够同时提升可见光透过率、抑制紫外光(UV)透射，并且在玻璃基底上具有优异附着性、耐磨性是目前研发的重点。本研究利用贝叶斯优化算法的机器学习方法指导超薄多层二氧化物ARC的结构设计。优化流程包括多层氧化物薄膜的参数化建模、采用传递矩阵法(TMM)的物理模拟，以及抗反射性能评估。经过优化的ARC采用100 nm SiO₂-10 nm TiO₂-10 nm SiO₂ (STS)叠层结构，使导电玻璃基底在400–800 nm范围内的透光率提升了9.2%。该结构应用在PSCs中获得了最高96.94%的外量子效率，使短路电流密度和光电转换效率均提升了4%，紫外光照持续300 h后仍保持有初始效率的81.2%，而标样组的效率降至~69%，表明STS ARC具备有效的紫外光过滤性能。STS ARC经过国际标准测试具有超过9H的硬度和ISO 0级以及ASTM 5B级附着度，满足太阳电池户外应用需求。除光学能量损失外，钙钛矿表面缺陷态富集导致非辐射复合能量损失，同时也是晶格降解的起始位点。因此，本文采用3-脒基吡啶氢碘酸盐(3-PyADI)对界面缺陷进行钝化修饰，协同将PSCs的效率提升至24.44%，未封装的器件在大气环境下放置1000 h后保留初始效率的93%。本研究所提出的增透减反薄膜与钙钛矿界面修饰协同实现器件性能和稳定性的同步提升，为钙钛矿太阳电池的产业化发展探索出具有前景且实用的路径。

关键词: 机器学习, 减反膜, 光管理策略, 钙钛矿太阳电池, 界面修饰

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

梁英, 邓羽恒, 余士律, 程家豪, 宋嘉伟, 姚俊, 杨亦辰, 张万雷, 周文靖, 张欣, 沈文剑, 梁桂杰, 李彬, 彭勇, 胡润, 李望南. 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[J]. 物理化学学报, 2025, 41(9), 100098. doi: 10.1016/j.actphy.2025.100098

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

链接本文: https://www.whxb.pku.edu.cn/CN/10.1016/j.actphy.2025.100098

https://www.whxb.pku.edu.cn/CN/Y2025/V41/I9/100098

图/表 5

参考文献 44

1	Best Research-Cell Efficiency Chart National Renewable Energy Laboratory (NREL), National Renewable Energy Laboratory (NREL), 2025. https://www.nrel.gov/pv/cell-efficiency.html (accessed: April 2025).
2	Xiao Z. ; Yan Y. Adv. Energy Mater. 2017, 7, 1701136. doi: 10.1002/aenm.201701136
3	Huang J. ; Yuan Y. ; Shao Y. ; Yan Y. Nat. Rev. Mater. 2017, 2, 17042. doi: 10.1038/natrevmats.2017.42
4	Li R. ; Liu H. ; Jiao Y. ; Qin S. ; Meng J. ; Song J. ; Yan R. ; Su H. ; Chen H. ; Shang Z. ; Zhao J. Acta Phys. -Chim. Sin. 2024, 40, 2311011. doi: 10.3866/PKU.WHXB202311011
5	De Bastiani M. ; Larini V. ; Montecucco R. ; Grancini G. Energy Environ. Sci. 2023, 16- 421. doi: 10.1039/D2EE03136A
6	Chen C. ; Zheng S. ; Song H. Chem. Soc. Rev. 2021, 50, 7250. doi: 10.1039/D0CS01488E
7	Da Y. ; Xuan Y. ; Li Q. Sol. Energy Mater. Sol. Cells 2018, 174, 206. doi: 10.1016/j.solmat.2017.09.002
8	Zhang Y. ; Jia X. ; Liu S. ; Zhang B. ; Lin K. ; Zhang J. ; Conibeer G. Sol. Energy Mater. Sol. Cells 2021, 225, 111073. doi: 10.1016/j.solmat.2021.111073
9	Ahmed Y. ; Feng X. ; Gao Y. ; Ding Y. ; Long C. ; Haider M. ; Li H. ; Li Z. ; Huang S. ; Saidaminov M.I. ; Yang J. Acta Phys. -Chim. Sin. 2024, 40, 2303057. doi: 10.3866/PKU.WHXB202303057
10	Wang Y. ; Zhang Z. ; Lan Y. ; Song Q. ; Li M. ; Song Y. Adv. Energy Mater. 2020, 10, 1902579. doi: 10.1002/aenm.201902579
11	Liu C. ; Yang Y. ; Chen H. ; Spanopoulos I. ; Bati A.S.R. ; Gilley I.W. ; Chen J. ; Maxwell A. ; Vishal B. ; Reynolds R.P. ; Wiggins T.E. ; Wang Z. ; Huang C. ; Fletcher J. ; Liu Y. ; Chen L.X. Nature 2024, 633, 359. doi: 10.1038/s41586-024-07764-8
12	Sha W.E.I. ; Zhang H. ; Wang Z.S. ; Zhu H.L. ; Ren X. ; Lin F. ; Jen A.K. -Y. ; Choy W.C.H. Adv. Energy Mater. 2018, 8, 1701586. doi: 10.1002/aenm.201701586
13	Liang Y. ; Jiao C. ; Zhou P. ; Li W. ; Zang Y. ; Liu Y. ; Yang G. ; Liu L. ; Cheng J. ; Liang G. ; Wang J. ; Zhong Z. ; Yan W. Bull. Chem. Soc. Jpn. 2023, 96, 148. doi: 10.1246/bcsj.20220307
14	Cho E. ; Son J.G. ; Park C.B. ; Kim I. ; Yuk D. ; Park J. ; Kim J.Y. ; Lee S. Adv. Funct. Mater. 2023, 33, 2301033. doi: 10.1002/adfm.202301033
15	Tavakoli M.M. ; Tsui K. -H. ; Zhang Q. ; He J. ; Yao Y. ; Li D. ; Fan Z. ACS Nano 2015, 9, 10287. doi: 10.1021/acsnano.5b04284
16	Gao Z. ; Lin G. ; Chen Y. ; Zheng Y. ; Sang N. ; Li Y. ; Chen L. ; Li M. Sol. Energy 2020, 205, 275. doi: 10.1016/j.solener.2020.05.065
17	Shahiduzzaman M. ; Hossain M.I. ; Visal S. ; Kaneko T. ; Qarony W. ; Umezu S. ; Tomita K. ; Iwamori S. ; Knipp D. ; Tsang Y.H. ; Akhtaruzzaman M. ; Nunzi J. -M. ; Taima T. ; Isomura M. Nano-Micro Lett. 2021, 13, 36. doi: 10.1007/s40820-020-00559-2
18	Choi J.S. ; Jang Y. ; Kim U. ; Choi M. ; Kang S.M. Adv. Energy Mater. 2022, 12, 2201520. doi: 10.1002/aenm.202201520
19	Raut H.K. ; Nair A.S. ; Dinachali S.S. ; Ganesh V.A. ; Walsh T.M. ; Ramakrishna S. Sol. Energy Mater. Sol. Cells 2013, 111, 9. doi: 10.1016/j.solmat.2012.12.023
20	Ye L. ; Zhang Y. ; Zhang X. ; Hu T. ; Ji R. ; Ding B. ; Jiang B. Energy Mater. Sol. Cells 2013, 111, 160. doi: 10.1016/j.solmat.2012.12.037
21	Kavakil İ. ; Kantarli K. Turk. J. Phys. 2002, 26, 349.
22	Wang Y. ; Wang H. ; Chen M. ; Wang P. ; Mao Y. ; Han W. ; Wang T. ; Liu D. Sci. China Mater. 2020, 64, 789. doi: 10.1007/s40843-020-1478-5
23	Liu Y. ; Tan X. ; Liang J. ; Han H. ; Xiang P. ; Yan W. Adv. Funct. Mater. 2023, 33, 2214271. doi: 10.1002/adfm.202214271
24	Liu Z. ; Rolston N. ; Flick A.C. ; Colburn T.W. ; Ren Z. ; Dauskardt R.H. ; Buonassisi T. Joule 2022, 6, 834. doi: 10.1016/j.joule.2022.03.003
25	Yan W. ; Liu Y. ; Zang Y. ; Cheng J. ; Wang Y. ; Chu L. ; Tan X. ; Liu L. ; Zhou P. ; Li W. Nano Energy 2022, 99, 107394. doi: 10.1016/j.nanoen.2022.107394
26	Palik E.D. Handbook of Optical Constants of Solids first ed. Burlington, USA: Academic Press, 1998, 719 795–798, 719-721.
27	Siefke T. ; Kroker S. ; Pfeiffer K. ; Puffky O. ; Dietrich K. ; Franta D. ; Ohlídal I. ; Szeghalmi A. ; Kley E. ; Tünnermann A. Adv. Opt. Mater. 2016, 4, 1780. doi: 10.1002/adom.201600250
28	Wasielewski R. ; Domaradzki J. ; Wojcieszak D. ; Kaczmarek D. ; Borkowska A. ; Prociow E.L. ; Ciszewski A. Appl. Surf. Sci. 2008, 254, 4396. doi: 10.1016/j.apsusc.2008.01.017
29	Jeong S.-H. ; Kim J.-K. ; Kim B.-S. ; Shim S.-H. ; Lee B.-T. Vacuum 2004, 76, 507. doi: 10.1016/j.vacuum.2004.06.003
30	Wei H.-S. ; Liu K.-T. ; Chang Y.-C. ; Chan C.-H. ; Lee C.-C. ; Kuo C.-C. Surf. Coat. Technol. 2017, 320, 377. doi: 10.1016/j.surfcoat.2016.12.025
31	Chen T. ; Xie J. ; Gao P. Adv. Energy Sustain. Res. 2022, 3, 2100218. doi: 10.1002/aesr.202100218
32	Bu T. ; Li J. ; Li H. ; Tian C. ; Su J. ; Tong G. ; Ono L.K. ; Wang C. ; Lin Z. ; Chai N. ; Zhang X.-L. ; Chang J. ; Lu J. ; Zhong J. ; Huang W. ; Qi Y. ; Cheng Y.-B. ; Huang F. Science 2021, 372, 1327. doi: 10.1126/science.abh1035
33	Gao Y. ; Liu C. ; He M. ; Zhang C. ; Liu L. ; Luo Q. ; Wu Y. ; Zhang H. ; Zhong X. ; Guo R. ; Xie Y. ; Wu S. ; Schropp R.E.I. ; Mai Y. Adv. Mater. 2024, 36, 2309310. doi: 10.1002/adma.202309310
34	Kim T. ; Park S. ; Iyer V. ; Shaheen B. ; Choudhry U. ; Jiang Q. ; Eichman G. ; Gnabasik R. ; Kelley K. ; Lawrie B. ; Zhu K. ; Liao B. Nat. Commun. 2023, 14, 1846. doi: 10.1038/s41467-023-37486-w
35	Kim S. ; Zhu K. Adv. Energy Mater. 2023, 13, 2300603. doi: 10.1002/aenm.202300603
36	Teale S. ; Degani M. ; Chen B. ; Sargent E.H. ; Grancini G. Nat. Energy 2024, 9, 779. doi: 10.1038/s41560-024-01529-3
37	Yang Y. ; Chen H. ; Liu C. ; Xu J. ; Huang C. ; Malliakas C.D. ; Wan H. ; Bati A.S.R. ; Wang Z. ; Reynolds R.P. ; Gilley I.W. ; Kitade S. ; Wiggins T.E. ; Zeiske S. ; Suragtkhuu S. ; Batmunkh M. ; Chen L.X. ; Chen B. ; Kanatzidis M.G. ; Sargent E.H. Science 2024, 386, 898. doi: 10.1126/science.adr2091
38	Chen B. ; Rudd P.N. ; Yang S. ; Yuan Y. ; Huang J. Chem. Soc. Rev. 2019, 48, 3842. doi: 10.1039/C8CS00853A
39	Liu L. ; Fang W.-H. ; Long R. ; Prezhdo O.V. J. Phys. Chem. Lett. 2018, 9, 1164. doi: 10.1021/acs.jpclett.8b00177
40	Kim G. ; Min H. ; Lee K.S. ; Lee D.Y. ; Yoon S.M. ; Seok S.I. Science 2020, 370, 108. doi: 10.1126/science.abc4417
41	Yang B. ; Bogachuk D. ; Suo J. ; Wagner L. ; Kim H. ; Lim J. ; Hinsch A. ; Boschloo G. ; Nazeeruddin M.K. ; Hagfeldt A. Chem. Soc. Rev. 2022, 51, 7509. doi: 10.1039/D2CS00278G
42	Kong Y. ; Shen W. ; Cai H. ; Dong W. ; Bai C. ; Zhao J. ; Huang F. ; Cheng Y.-B. ; Zhong J. Adv. Funct. Mater. 2023, 33, 2300932. doi: 10.1002/adfm.202300932
43	Shen W. ; Cai H. ; Kong Y. ; Dong W. ; Bai C. ; Liang G. ; Li W. ; Zhao J. ; Huang F. ; Cheng Y. ; Zhong J. Small 2023, 19, 2302194. doi: 10.1002/smll.202302194
44	Zhou J. ; Li M. ; Wang S. ; Tan L. ; Liu Y. ; Jiang C. ; Zhao X. ; Ding L. ; Yi C. Nano Energy 2022, 95, 107036. doi: 10.1016/j.nanoen.2022.107036

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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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

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

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