Machine learning-assisted SCAPS device simulation for photovoltaic parameters prediction of CsSnI3 3 perovskite solar cells

被引:3
作者
Chabri, I. [1 ]
Said, M. [1 ]
El-Allaly, Ed. [2 ]
Oubelkacem, A. [1 ,2 ]
机构
[1] Univ Moulay Ismail, Fac Sci, Phys Dept, Lab Mat Phys & Syst Modelling,LP2MS, Meknes, Morocco
[2] Moulay Ismail Univ, Fac Sci, Comp Sci Dept, Meknes, Morocco
关键词
Perovskite solar cells; Machine learning; SCAPS-1D; Photovoltaic parameters; RANDOM FOREST REGRESSION; PERFORMANCE; DRIVEN;
D O I
10.1016/j.mtcomm.2024.110585
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Perovskite solar cells (PSCs) have garnered significant research attention because of their remarkable surge in power conversion efficiency (PCE) surpassing 26% within a short timeframe. However, the widespread adoption of these highly efficient PSCs is hampered by the inherent toxicity associated with lead (Pb)-based perovskites, which currently dominate the field. Researchers have explored alternative cations like Ge 2+ and Sn 2+ Owing to their identical oxidation states to Pb 2+ , paving the way for lead-free PSC development. By examining the critical factors influencing CsSnI3 3 thin film solar cells' performance as well as their interdependencies, this work seeks to improve the efficiency of these devices. To do so, machine learning algorithms (ML) are employed to figure out the critical factors influencing the completion of CsSnI3 3 solar cells. A new dataset of 7870 data points is generated through simulations with SCAPS-1D to effectively depict the perovskite solar cell. Five ML regression algorithms are thereafter utilized to predict the photovoltaic parameters of solar cells, such as the fill factor (FF), open circuit voltage (VOC), OC ), short circuit current (JSC), SC ), and PCE. The study shows that random forest (RF) and decision tree (DT) are the best performing algorithms, among which DT produces high accuracy and low error for predicting the PCE with a coefficient of determination R2 2 of 0.99, Pearson's coefficient (R) of 0.99, Spearman's correlation coefficient (p) of 0.99 and root mean square error of 0.08 on the testing set. Finally, this study offers valuable guidance for further experiment optimization by shedding light on the optimal device dimensions and essential components.
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页数:16
相关论文
共 83 条
[1]   Deep neural networks for the quantile estimation of regional renewable energy production [J].
Alcantara, Antonio ;
Galvan, Ines M. ;
Aler, Ricardo .
APPLIED INTELLIGENCE, 2023, 53 (07) :8318-8353
[2]   Machine Learning for Halide Perovskite Materials ABX3 (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy [J].
Alhashmi, Afnan ;
Kanoun, Mohammed Benali ;
Goumri-Said, Souraya .
MATERIALS, 2023, 16 (07)
[3]  
Angizi S., 2023, Scaps empowered machine learning modelling of perovskite solar cells: Predictive design of active layer and hole transport materials, DOI [10.2139/ssrn.4332470, DOI 10.2139/SSRN.4332470]
[4]   GIS-assisted modeling of wind farm site selection based on support vector regression [J].
Asadi, Meysam ;
Pourhossein, Kazem ;
Mohammadi-Ivatloo, Behnam .
JOURNAL OF CLEANER PRODUCTION, 2023, 390
[5]   Aspects of effectiveness and significance: The use of machine learning methods to study CuIn1-xGaxSe2 solar cells [J].
Bandaru, Narendra ;
Enduri, Murali Krishna ;
Reddy, Ch. Venkata ;
Kakarla, Raghava Reddy .
SOLAR ENERGY, 2023, 263
[6]   Screening of novel halide perovskites for photocatalytic water splitting using multi-fidelity machine learning [J].
Biswas, Maitreyo ;
Desai, Rushik ;
Mannodi-Kanakkithodi, Arun .
PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2024, 26 (35) :23177-23188
[7]   Modelling polycrystalline semiconductor solar cells [J].
Burgelman, M ;
Nollet, P ;
Degrave, S .
THIN SOLID FILMS, 2000, 361 :527-532
[8]   Predicting the energy consumption in buildings using the optimized support vector regression model [J].
Cai, Wei ;
Wen, Xiaodong ;
Li, Chaoen ;
Shao, Jingjing ;
Xu, Jianguo .
ENERGY, 2023, 273
[9]   Enhance stability of ?-CsSnI3-based PSCs by (?-CsSnI3-Cs2SnI6) heterojunction [J].
Chabri, I. ;
Benhouria, Y. ;
Oubelkacem, A. ;
Kaiba, A. ;
Essaoudi, I. ;
Ainane, A. .
SOLAR ENERGY MATERIALS AND SOLAR CELLS, 2023, 259
[10]   Cs2AgBiBr6-based perovskite solar cell: A novel combination of ITO/CdS/Cs2AgBiBr6/CuAlO2/Pt, with inorganic charge transport layers [J].
Chabri, I. ;
Benhouria, Y. ;
Oubelkacem, A. ;
Kaiba, A. ;
Essaoudi, I. ;
Ainane, A. .
OPTIK, 2023, 274