Efficient Photoelectric Coupling Simulation and Machine Learning Study of Perovskite Solar Cells (Invited)

Ruiying Kong; Yijun Wei; Jiacheng Chen; Tianshu Ma; Yaohui Zhan; Xiaofeng Li

doi:10.3788/LOP232375

[1] Green M A, Bremner S P. Energy conversion approaches and materials for high-efficiency photovoltaics[J]. Nature Materials, 16, 23-34(2017).

[2] Dunlap-Shohl W A, Zhou Y Y, Padture N P et al. Synthetic approaches for halide perovskite thin films[J]. Chemical Reviews, 119, 3193-3295(2019).

[3] Wang H, Kim D H. Perovskite-based photodetectors: materials and devices[J]. Chemical Society Reviews, 46, 5204-5236(2017).

[4] Zhou Y, Chen J, Bakr O M et al. Metal-doped lead halide perovskites: synthesis, properties, and optoelectronic applications[J]. Chemistry of Materials, 30, 6589-6613(2018).

[5] Li Y, Yang Y L, Chen L J et al. Research progress on electron transport layer of inverted perovskite solar cells[J]. Laser & Optoelectronics Progress, 60, 1500006(2023).

[6] Lü J Q, Zhang N, Yin P et al. Research progress on optically designed solar photovoltaic concentrators[J]. Laser & Optoelectronics Progress, 56, 230002(2019).

[7] An Y D, Sheng C X, Li X F. Radiative cooling of solar cells: opto-electro-thermal physics and modeling[J]. Nanoscale, 11, 17073-17083(2019).

[8] Tao L P. Study on performance of organic heterojunction/perovskite composite photovoltaic devices[D](2023).

[9] Wang Y Q. Design and performance study of ultra-thin perovskite solar cells[D](2023).

[10] Polman A, Knight M, Garnett E C et al. Photovoltaic materials: present efficiencies and future challenges[J]. Science, 352, aad4424(2016).

[11] Polman A, Atwater H A. Photonic design principles for ultrahigh-efficiency photovoltaics[J]. Nature Materials, 11, 174-177(2012).

[12] Ren X G, Wang Z S, Sha W E I et al. Exploring the way to approach the efficiency limit of perovskite solar cells by drift-diffusion model[J]. ACS Photonics, 4, 934-942(2017).

[13] Sha W E I, Ren X G, Chen L Z et al. The efficiency limit of CH3NH3PbI3 perovskite solar cells[J]. Applied Physics Letters, 106, 221104(2015).

[14] An Y D, Wang C L, Cao G Y et al. Heterojunction perovskite solar cells: opto-electro-thermal physics, modeling, and experiment[J]. ACS Nano, 14, 5017-5026(2020).

[15] Kossyvakis D N, Hristoforou E V, Falaras P et al. Optimum coupling of photovoltaic devices and Peltier coolers for improved performance and stability[J]. International Journal of Sustainable Energy, 41, 1667-1693(2022).

[16] Kamarudin M A, Sahamir S R, Ripolles T S et al. Energy level alignment studies in tin perovskite solar cells through incorporation of inorganic cation and charge transport layer selection[J]. Organic Electronics, 113, 106712(2023).

[17] Li Y, Lü Z Y, Gou T et al. Temperature-dependent optical properties and photovoltaic device design of Cs-doped FAPbBr3 thin films[J]. Acta Optica Sinica, 43, 2331004(2023).

[18] Schmidt H J, Bröcker T. Maxwell equations with spin density[J]. European Journal of Physics, 44, 035201(2023).

[19] Heinreichsberger O, Selberherr S, Stiftinger M et al. Fast iterative solution of carrier continuity equations for three-dimensional device simulation[J]. SIAM Journal on Scientific and Statistical Computing, 13, 289-306(1992).

[20] Shang A X, An Y D, Ma D et al. Optoelectronic insights into the photovoltaic losses from photocurrent, voltage, and energy perspectives[J]. AIP Advances, 7, 085019(2017).

[21] Gao Y L. Generalized finite difference method for Poisson equation based on arbitrary polygon mesh[J]. Journal of Xi’an University (Natural Science Edition), 26, 1-6(2023).

[22] Liu Y Q. Poisson process and thermal performance simulation of lithium battery materials based on mathematical model[J]. Information Recording Materials, 24, 166-168, 172(2023).

[23] Xu L J, Liu W Z, Liu H H et al. Heat generation and mitigation in silicon solar cells and modules[J]. Joule, 5, 631-645(2021).

[24] Li X F, Hylton N P, Giannini V et al. Bridging electromagnetic and carrier transport calculations for three-dimensional modelling of plasmonic solar cells[J]. Optics Express, 19, A888-A896(2011).

[25] Liu Y M, Yan W S, Han S C et al. How machine learning predicts and explains the performance of perovskite solar cells[J]. Solar RRL, 6, 2101100(2022).

[26] Saidi W A, Shadid W, Castelli I E. Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network[J]. NPJ Computational Materials, 6, 36(2020).

[27] Liu Y M, Yan W S, Zhu H et al. Study on bandgap predications of ABX3-type perovskites by machine learning[J]. Organic Electronics, 101, 106426(2022).

[28] Hsu J W P, Xu W J. Accelerate process optimization in perovskite solar cell manufacturing with machine learning[J]. Matter, 5, 1334-1336(2022).

[29] Li J X, Pradhan B, Gaur S et al. Predictions and strategies learned from machine learning to develop high-performing perovskite solar cells[J]. Advanced Energy Materials, 9, 1970181(2019).

[30] Yan W S, Liu Y M, Zang Y et al. Machine learning enabled development of unexplored perovskite solar cells with high efficiency[J]. Nano Energy, 99, 107394(2022).

[31] Shang A X, Li X F. Photovoltaic devices: opto-electro-thermal physics and modeling[J]. Advanced Materials, 29, 1603492(2017).

[32] Li X F, Hylton N P, Giannini V et al. Multi-dimensional modeling of solar cells with electromagnetic and carrier transport calculations[J]. Progress in Photovoltaics: Research and Applications, 21, 109-120(2013).

[33] Chen Z X, Cai J, Zhang H B et al. Fiber nonlinear damage compensation based on multi-source domain transfer learning[J/OL]. Research on Optical Communication, 1-9. http://kns.cnki.net/kcms/detail/42.1266

[34] Fu L L, Zhang Z Q, Xu H M et al. Research progress of optical functional glass based on machine learning[J/OL]. Laser & Optoelectronics Progress, 1-12. https://www.?opticsjournal.?net/Articles/L231278/EarlyPosting

[35] Ma Q X, Li C, Li T Y et al. Quantitative analysis of binary and ternary mixtures based on terahertz spectroscopy and machine learning algorithm[J]. Laser & Optoelectronics Progress, 59, 1930003(2022).

[36] Huang Q M, Chen Y K, Liu X Y et al. Fast Positioning of Brillouin Optical Time Domain Reflectometry Frequency Shift and Enhancement of Spatial Resolution Using Maximum-Seeking Method[J]. Acta Optica Sinica, 43, 1406004(2023).

[37] Agiorgousis M L, Sun Y Y, Choe D H et al. Machine learning augmented discovery of chalcogenide double perovskites for photovoltaics[J]. Advanced Theory and Simulations, 3, 1800173(2020).

[38] Wu D, Zhang H C, Liu H C et al. Revealing the hidden mechanism of enhanced responsivity of doped p-i-n perovskite photodiodes via coupled opto-electronic model[J]. Molecules, 27, 6223(2022).

[39] Chen J L, Feng M J, Zha C Y et al. Machine learning-driven design of promising perovskites for photovoltaic applications: a review[J]. Surfaces and Interfaces, 35, 102470(2022).

[40] Han Q, Liu H, Guo F Y et al. Research progress on Cs2AgBiBr6 halide double-perovskite solar cells[J]. Laser & Optoelectronics Progress, 60, 0700004(2023).

[41] Luo J, Li X, Zhang X Y et al. Deep-learning-enabled inverse engineering of multi-wavelength invisibility-to-superscattering switching with phase-change materials[J]. Optics Express, 29, 10527-10537(2021).

[42] An Y D, Shang A X, Cao G Y et al. Perovskite solar cells: optoelectronic simulation and optimization[J]. Solar RRL, 2, 1870227(2018).

[43] Levtchenko A, Lachaume R, Michallon J et al. Coupling optical and electrical modelling for the study of a‐Si:H‐based nanowire array solar cells[J]. Physica Status Solidi(C), 14, 1700181(2017).

[44] Shang A X. Study on optics and carrier dynamics of nanostructured photovoltaic devices[D](2018).

[45] Zhang C, Cao G Y, Wu S L et al. Thermodynamic loss mechanisms and strategies for efficient hot-electron photoconversion[J]. Nano Energy, 55, 164-172(2019).

[46] Wang C L, Zhao Y, Ma T S et al. A universal close-space annealing strategy towards high-quality perovskite absorbers enabling efficient all-perovskite tandem solar cells[J]. Nature Energy, 7, 744-753(2022).

[47] Liang G Q, Zhang J. A machine learning model for screening thermodynamic stable lead-free halide double perovskites[J]. Computational Materials Science, 204, 111172(2022).

[48] Krishna R V, Laxmi , Mahapatra B et al. Effect of electrical parameters on lead-based perovskite solar cell for high-efficiency performance[J]. Optical and Quantum Electronics, 54, 1-12(2022).