Perovskites informatics: Studying the impact of thicknesses, doping, and defects on the perovskite solar cell efficiency using a machine learning algorithm
The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide...
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| Vydané v: | International journal of numerical modelling Ročník 37; číslo 2 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Chichester, UK
John Wiley & Sons, Inc
01.03.2024
Wiley Subscription Services, Inc |
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| ISSN: | 0894-3370, 1099-1204 |
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| Abstract | The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr3, and CsPbCl3, respectively. |
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| AbstractList | The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr3, and CsPbCl3, respectively. The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI 3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr 3 , and CsPbCl 3 , respectively. |
| Author | Amer, Fathy Z. Ismail, Zahraa S. Abdellatif, Sameh O. Sawires, Eman F. |
| Author_xml | – sequence: 1 givenname: Zahraa S. surname: Ismail fullname: Ismail, Zahraa S. organization: British University in Egypt (BUE) – sequence: 2 givenname: Eman F. surname: Sawires fullname: Sawires, Eman F. organization: Helwan University – sequence: 3 givenname: Fathy Z. surname: Amer fullname: Amer, Fathy Z. organization: Helwan University – sequence: 4 givenname: Sameh O. orcidid: 0000-0001-8677-9497 surname: Abdellatif fullname: Abdellatif, Sameh O. email: sameh.osama@bue.edu.eg organization: British University in Egypt (BUE) |
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| Cites_doi | 10.1021/acs.chemrev.6b00136 10.1088/1742-6596/1299/1/012129 10.1021/acsaem.8b01263 10.1021/acsphotonics.8b00124 10.1038/s41578-019-0080-9 10.1063/1.5128016 10.1002/aenm.202201463 10.1016/j.micromeso.2018.01.011 10.1021/acsanm.2c01550 10.1109/JPHOTOV.2020.2965399 10.1016/j.joule.2018.04.012 10.1039/D1EE02882K 10.1016/j.solener.2021.04.030 10.1038/s41467‐018‐06915‐6 10.1002/smtd.201600018 10.1016/j.solener.2022.01.060 10.1016/j.mattod.2022.11.002 10.1016/j.nanoen.2019.01.034 10.1016/j.solener.2021.09.030 10.1038/s43246-022-00291-x 10.1016/j.joule.2021.04.008 10.1016/j.solener.2022.08.001 10.1007/s11801‐022‐1115‐9 10.1016/j.solener.2019.11.005 10.1016/j.solener.2020.01.081 10.1016/j.nanoen.2022.107394 10.1039/C6TA09582H 10.1016/j.jallcom.2017.02.147 10.1021/acsami.7b18902 10.1002/advs.201700780 10.1016/j.optmat.2022.112075 10.1109/JPHOTOV.2021.3086443 10.1038/s41560-018-0190-4 10.1002/aenm.201501066 10.1109/JPHOTOV.2022.3226711 10.1016/j.nanoen.2020.105546 10.1116/6.0000718 10.1002/adma.201902851 10.1002/smll.201800682 10.1016/j.jechem.2021.07.020 10.1021/acsnano.8b07850 10.1039/C8TA08900K 10.1016/j.joule.2022.03.003 10.1002/aenm.201901891 10.1016/j.solener.2020.09.003 10.1016/j.nanoen.2018.12.038 10.1039/D1TC05851G 10.1117/1.JPE.12.022202 10.1016/j.nanoen.2018.11.069 10.1002/adfm.201909972 10.1016/j.solmat.2019.110284 |
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| References_xml | – volume: 9 year: 2019 article-title: Predictions and strategies learned from machine learning to develop high‐performing perovskite solar cells publication-title: Adv Energy Mater – volume: 1 start-page: 158 year: 2022 end-page: 164 article-title: Experimental studies for glass light transmission degradation in solar cells due to dust accumulation using effective optical scattering parameters and machine learning algorithm publication-title: IEEE J Photovolt – volume: 5 start-page: 2066 year: 2017 end-page: 2072 article-title: Dimension engineering on cesium lead iodide for efficient and stable perovskite solar cells publication-title: J Mater Chem A – volume: 30 year: 2020 article-title: Controlled n‐doping in air‐stable CsPbI2Br perovskite solar cells with a record efficiency of 16.79% publication-title: Adv Funct Mater – volume: 264 start-page: 84 year: 2018 end-page: 91 article-title: Refractive index and scattering of porous TiO films publication-title: Micropor Mesopor Mater – volume: 57 start-page: 74 year: 2019 end-page: 93 article-title: A comprehensive review of flexible piezoelectric generators based on organic‐inorganic metal halide perovskites publication-title: Nano Energy – volume: 5 year: 2015 article-title: Beyond efficiency: the challenge of stability in mesoscopic perovskite solar cells publication-title: Adv Energy Mater – year: 2021 – volume: 5 year: 2018 article-title: Organic/inorganic metal halide perovskite optoelectronic devices beyond solar cells publication-title: Adv Sci – volume: 228 start-page: 689 year: 2021 end-page: 699 article-title: Machine learning stability and band gap of lead‐free halide double perovskite materials for perovskite solar cells publication-title: Solar Energy – volume: 31 year: 2019 article-title: Review on recent progress of all‐inorganic metal halide perovskites and solar cells publication-title: Adv Mater – volume: 10 start-page: 7145 year: 2018 article-title: All‐ambient processed binary CsPbBr ‐CsPb Br perovskites with synergistic enhancement for high‐efficiency Cs‐Pb‐Br‐based solar cells publication-title: ACS Appl Mater Interfaces – volume: 12 year: 2022 article-title: Machine‐learning modeling for ultra‐stable high‐efficiency perovskite solar cells publication-title: Adv Energy Mater – volume: 6 start-page: 24324 year: 2018 end-page: 24329 article-title: All‐inorganic CsPbBr perovskite solar cell with 10.26% efficiency by spectra engineering publication-title: J Mater Chem A – volume: 66 start-page: 74 year: 2022 end-page: 90 article-title: Is machine learning redefining the perovskite solar cells? publication-title: J Energy Chem – volume: 4 start-page: 169 year: 2019 end-page: 188 article-title: Metal halide perovskite nanostructures for optoelectronic applications and the study of physical properties publication-title: Nat Rev Mater – volume: 264 start-page: 84 year: 2018 article-title: Refractive index and scattering of porous TiO films. Microporous and mesoporous materials publication-title: Micropor Mesopor Mater – volume: 12 year: 2022 article-title: Investigating the variation in the optical properties of TiO thin‐film utilized in bifacial solar cells using machine learning algorithm publication-title: J Photon Energy – volume: 10 start-page: 522 year: 2020 end-page: 530 article-title: Transparency and diffused light efficiency of dye‐sensitized solar cells: tuning and a new figure of merit publication-title: IEEE J Photovolt – volume: 56 start-page: 770 year: 2019 end-page: 791 article-title: Performance analysis of perovskite solar cells in 2013–2018 using machine‐learning tools publication-title: Nano Energy – volume: 5 start-page: 2970 year: 2018 end-page: 2977 article-title: Research direction toward theoretical efficiency in perovskite solar cells publication-title: ACS Photonics – volume: 2 start-page: 1500 year: 2018 article-title: Graded bandgap CsPbI Br perovskite solar cells with a stabilized efficiency of 14.4% publication-title: Joule – volume: 152 year: 2020 article-title: Understanding size dependence of phase stability and band gap in CsPbI3 perovskite nanocrystals publication-title: J Chem Phys – volume: 14 year: 2018 article-title: Photonics and optoelectronics of 2D metal‐halide perovskites publication-title: Small – volume: 11 start-page: 1222 year: 2021 end-page: 1235 article-title: Investigating the tradeoff between transparency and efficiency in semitransparent bifacial Mesosuperstructured solar cells for millimeter‐scale applications publication-title: IEEE J Photovolt – volume: 80 year: 2021 article-title: Critical review of machine learning applications in perovskite solar research publication-title: Nano Energy – volume: 6 start-page: 834 year: 2022 end-page: 849 article-title: Machine learning with knowledge constraints for process optimization of open‐air perovskite solar cell manufacturing publication-title: Joule – volume: 39 year: 2021 article-title: Evidence of improved power conversion efficiency in lead‐free CsGeI based perovskite solar cell heterostructure via scaps simulation publication-title: J Vacuum Sci Technol B – volume: 1 year: 2017 article-title: Two‐dimensional metal halide perovskites: theory, synthesis, and optoelectronics publication-title: Small Methods – volume: 1 start-page: 6227 year: 2018 end-page: 6233 article-title: Room‐temperature‐sputtered Nanocrystalline nickel oxide as hole transport layer for p–i–n perovskite solar cells publication-title: ACS Appl Energy Mater – volume: 1299 issue: 1 year: 2019 article-title: First principles calculations of the optoelectronic properties of magnesium substitutes in Lead based ABX3 compounds publication-title: J Phys – volume: 244 start-page: 516 year: 2022 end-page: 535 article-title: Stability and efficiency issues, solutions and advancements in perovskite solar cells: A review publication-title: Solar Energy – volume: 194 start-page: 886 year: 2019 end-page: 892 article-title: Numerical study of Cs2TiX6 (X = Br−, I−, F− and Cl−) based perovskite solar cell using SCAPS‐1D device simulation publication-title: Solar Energy – volume: 13 start-page: 1772 year: 2019 article-title: High‐performance all‐inorganic CsPbCl perovskite nanocrystal photodetectors with superior stability publication-title: ACS Nano – volume: 9 start-page: 4544 year: 2018 article-title: All‐inorganic cesium lead iodide perovskite solar cells with stabilized efficiency beyond 15 publication-title: Nat Commun – volume: 198 start-page: 454 year: 2020 end-page: 460 article-title: Simulated development and optimized performance of CsPbI3 based all‐inorganic perovskite solar cells publication-title: Solar Energy – volume: 116 start-page: 12956 year: 2016 end-page: 13008 article-title: Intriguing optoelectronic properties of metal halide perovskites publication-title: Chem Rev – volume: 99 year: 2022 article-title: Machine learning enabled development of unexplored perovskite solar cells with high efficiency publication-title: Nano Energy – volume: 209 start-page: 79 year: 2020 article-title: Optimizing the working mechanism of the CsPbBr ‐based inorganic perovskite solar cells for enhanced efficiency publication-title: Solar Energy – volume: 205 year: 2020 article-title: Machine learning analysis on stability of perovskite solar cells publication-title: Solar Energy Mater Solar Cells – volume: 705 start-page: 828 year: 2017 end-page: 839 article-title: Structural, electronic and optical properties of CsPbX3 (X = Cl, Br, I) for energy storage and hybrid solar cell applications publication-title: J Alloys Compd – volume: 58 start-page: 175 year: 2019 article-title: Chlorine doping for black γ‐CsPbI solar cells with stabilized efficiency beyond 16% publication-title: Nano Energy – volume: 18 start-page: 148 year: 2022 end-page: 151 article-title: Optoelectronic devices informatics: optimizing DSSC performance using random‐forest machine learning algorithm publication-title: Optoelectronics Lett – volume: 3 start-page: 828 year: 2018 end-page: 838 article-title: Opportunities and challenges for tandem solar cells using metal halide perovskite semiconductors publication-title: Nat Energy – volume: 5 start-page: 10097 year: 2022 end-page: 10117 article-title: Charge transfer in Photoexcited cesium–Lead halide perovskite nanocrystals: review of materials and applications publication-title: ACS Appl Nano Mater – volume: 125 year: 2022 article-title: Numerical modeling and performance optimization of carbon‐based hole transport layer free perovskite solar cells publication-title: Opt Mater – volume: 3 start-page: 1 year: 2022 article-title: Improving the stability of inverted perovskite solar cells towards commercialization publication-title: Commun Mater – year: 2020 – volume: 15 start-page: 13 year: 2022 end-page: 55 article-title: Development of encapsulation strategies towards the commercialization of perovskite solar cells publication-title: Energ Environ Sci – volume: 221 start-page: 99 year: 2021 end-page: 108 article-title: Simulation and optimization studies on CsPbI3 based inorganic perovskite solar cells publication-title: Solar Energy – volume: 10 start-page: 4999 year: 2022 end-page: 5023 article-title: Strategies for highly efficient and stable cesium lead iodide perovskite photovoltaics: mechanisms and processes publication-title: J Mater Chem C – volume: 61 start-page: 191 year: 2022 end-page: 217 article-title: Inorganic lead‐based halide perovskites: From fundamental properties to photovoltaic applications publication-title: Mater Today – volume: 233 start-page: 421 year: 2022 end-page: 434 article-title: Review on efficiency improvement effort of perovskite solar cell publication-title: Solar Energy – volume: 5 start-page: 1033 year: 2021 end-page: 1035 article-title: Development of perovskite solar cells with >25% conversion efficiency publication-title: Joule – ident: e_1_2_12_7_1 doi: 10.1021/acs.chemrev.6b00136 – ident: e_1_2_12_13_1 doi: 10.1088/1742-6596/1299/1/012129 – ident: e_1_2_12_43_1 doi: 10.1021/acsaem.8b01263 – ident: e_1_2_12_26_1 doi: 10.1021/acsphotonics.8b00124 – ident: e_1_2_12_6_1 doi: 10.1038/s41578-019-0080-9 – ident: e_1_2_12_22_1 doi: 10.1063/1.5128016 – ident: e_1_2_12_37_1 doi: 10.1002/aenm.202201463 – ident: e_1_2_12_40_1 doi: 10.1016/j.micromeso.2018.01.011 – ident: e_1_2_12_18_1 doi: 10.1021/acsanm.2c01550 – ident: e_1_2_12_39_1 doi: 10.1109/JPHOTOV.2020.2965399 – ident: e_1_2_12_55_1 doi: 10.1016/j.joule.2018.04.012 – ident: e_1_2_12_17_1 doi: 10.1039/D1EE02882K – ident: e_1_2_12_48_1 doi: 10.1016/j.solener.2021.04.030 – ident: e_1_2_12_52_1 doi: 10.1038/s41467‐018‐06915‐6 – ident: e_1_2_12_41_1 doi: 10.1016/j.micromeso.2018.01.011 – volume-title: International Conference on Interactive Collaborative and Blended Learning year: 2021 ident: e_1_2_12_45_1 – ident: e_1_2_12_4_1 doi: 10.1002/smtd.201600018 – ident: e_1_2_12_10_1 doi: 10.1016/j.solener.2022.01.060 – ident: e_1_2_12_20_1 doi: 10.1016/j.mattod.2022.11.002 – ident: e_1_2_12_53_1 doi: 10.1016/j.nanoen.2019.01.034 – ident: e_1_2_12_38_1 doi: 10.1016/j.solener.2021.09.030 – ident: e_1_2_12_50_1 – ident: e_1_2_12_16_1 doi: 10.1038/s43246-022-00291-x – ident: e_1_2_12_11_1 doi: 10.1016/j.joule.2021.04.008 – ident: e_1_2_12_14_1 doi: 10.1016/j.solener.2022.08.001 – ident: e_1_2_12_30_1 doi: 10.1007/s11801‐022‐1115‐9 – ident: e_1_2_12_46_1 doi: 10.1016/j.solener.2019.11.005 – ident: e_1_2_12_49_1 doi: 10.1016/j.solener.2020.01.081 – ident: e_1_2_12_12_1 doi: 10.1016/j.nanoen.2022.107394 – ident: e_1_2_12_21_1 doi: 10.1039/C6TA09582H – ident: e_1_2_12_24_1 doi: 10.1016/j.jallcom.2017.02.147 – ident: e_1_2_12_54_1 doi: 10.1021/acsami.7b18902 – ident: e_1_2_12_9_1 doi: 10.1002/advs.201700780 – ident: e_1_2_12_27_1 doi: 10.1016/j.optmat.2022.112075 – ident: e_1_2_12_42_1 doi: 10.1109/JPHOTOV.2021.3086443 – ident: e_1_2_12_8_1 doi: 10.1038/s41560-018-0190-4 – ident: e_1_2_12_15_1 doi: 10.1002/aenm.201501066 – ident: e_1_2_12_29_1 doi: 10.1109/JPHOTOV.2022.3226711 – ident: e_1_2_12_31_1 doi: 10.1016/j.nanoen.2020.105546 – ident: e_1_2_12_47_1 doi: 10.1116/6.0000718 – ident: e_1_2_12_3_1 doi: 10.1002/adma.201902851 – ident: e_1_2_12_5_1 doi: 10.1002/smll.201800682 – ident: e_1_2_12_32_1 doi: 10.1016/j.jechem.2021.07.020 – ident: e_1_2_12_57_1 doi: 10.1021/acsnano.8b07850 – ident: e_1_2_12_23_1 doi: 10.1039/C8TA08900K – ident: e_1_2_12_35_1 doi: 10.1016/j.joule.2022.03.003 – ident: e_1_2_12_36_1 doi: 10.1002/aenm.201901891 – ident: e_1_2_12_56_1 doi: 10.1016/j.solener.2020.09.003 – ident: e_1_2_12_2_1 doi: 10.1016/j.nanoen.2018.12.038 – ident: e_1_2_12_19_1 doi: 10.1039/D1TC05851G – ident: e_1_2_12_51_1 doi: 10.1117/1.JPE.12.022202 – ident: e_1_2_12_34_1 doi: 10.1016/j.nanoen.2018.11.069 – ident: e_1_2_12_28_1 doi: 10.1002/adfm.201909972 – ident: e_1_2_12_44_1 – ident: e_1_2_12_33_1 doi: 10.1016/j.solmat.2019.110284 – ident: e_1_2_12_25_1 |
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| SubjectTerms | Algorithms Cesium cesium lead halides data regression Datasets Defects Design parameters Doping Electron transport Energy conversion efficiency Lead compounds Machine learning Mathematical models Metal halides optimization Perovskites Photovoltaic cells power conversion efficiency random‐forest algorithm Solar cells Thickness Thin films |
| Title | Perovskites informatics: Studying the impact of thicknesses, doping, and defects on the perovskite solar cell efficiency using a machine learning algorithm |
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