A. Makarigakis and B. Jimenez-Cisneros, “UNESCO’s Contribution to Face Global Water Challenges,” Water, vol. 11, no. 2, p. 388, Feb. 2019, doi: 10.3390/w11020388.
 A. Pareek, R. Dom, J. Gupta, J. Chandran, V. Adepu, and P. H. Borse, “Insights into renewable hydrogen energy: Recent advances and prospects,” Mater. Sci. Energy Technol., vol. 3, pp. 319–327, 2020, doi: 10.1016/j.mset.2019.12.002.
 Z. Xiu, “Preparation of tin-based perovskite solar cell thin films assisted by stannous fluoride,” Energy Reports, vol. 8, pp. 1079–1089, Jul. 2022, doi: 10.1016/j.egyr.2022.02.049.
 R. Ghasempour, S. Ghanbari Motlagh, M. Montazeri, and R. Shirmohammadi, “Deployment a hybrid renewable energy system for enhancing power generation and reducing water evaporation of a dam,” Energy Reports, vol. 8, pp. 10272–10289, 2022, doi: 10.1016/j.egyr.2022.07.177.
 W. A. Badawy, “A review on solar cells from Si-single crystals to porous materials and quantum dots,” J. Adv. Res., vol. 6, no. 2, pp. 123–132, Mar. 2015, doi: 10.1016/j.jare.2013.10.001.
 M. L. Brongersma, Y. Cui, and S. Fan, “Light management for photovoltaics using high-index nanostructures,” Nat. Mater., vol. 13, no. 5, pp. 451–460, 2014, doi: 10.1038/nmat3921.
 T. Shiyani and T. Bagchi, “Hybrid nanostructures for solar-energy-conversion applications,” Nanomater. Energy, vol. 9, no. 1, pp. 39–46, Jun. 2020, doi: 10.1680/jnaen.19.00029.
 F. Sabahi, “Fuzzy Adaptive Granulation Multi-Objective Multi-microgrid Energy Management,” J. AI Data Min., vol. 8, no. 4, pp. 481–489, 2019, doi: 10.22044/JADM.2019.6985.1828.
 N. Bigdeli and H. S. Lafmejani, “Dynamic characterization and predictability analysis of wind speed and wind power time series in Spain wind farm,” J. Artif. Intell. Data Min., vol. 4, no. 1, pp. 103–116, 2016, doi: 10.5829/idosi.JAIDM.2016.04.01.12.
 K. Choudhary, M. Bercx, J. Jiang, R. Pachter, D. Lamoen, and F. Tavazza, “Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods,” Chem. Mater., vol. 31, no. 15, pp. 5900–5908, Aug. 2019, doi: 10.1021/acs.chemmater.9b02166.
 G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang, “Physics-informed machine learning,” Nat. Rev. Phys., vol. 3, no. 6, pp. 422–440, May 2021, doi: 10.1038/s42254-021-00314-5.
 Y. Liu, O. C. Esan, Z. Pan, and L. An, “Machine learning for advanced energy materials,” Energy AI, vol. 3, p. 100049, 2021, doi: 10.1016/j.egyai.2021.100049.
 Z. Lu, “Computational discovery of energy materials in the era of big data and machine learning: A critical review,” Mater. Reports Energy, vol. 1, no. 3, p. 100047, 2021, doi: 10.1016/j.matre.2021.100047.
 L. Zhang, M. He, and S. Shao, “Machine learning for halide perovskite materials,” Nano Energy, vol. 78, no. August, 2020, doi: 10.1016/j.nanoen.2020.105380.
 L. Ju, M. Li, L. Tian, P. Xu, and W. Lu, “Accelerated discovery of high-efficient N-annulated perylene organic sensitizers for solar cells via machine learning and quantum chemistry,” Mater. Today Commun., vol. 25, no. April, p. 101604, 2020, doi: 10.1016/j.mtcomm.2020.101604.
 J. M. Howard, E. M. Tennyson, B. R. A. Neves, and M. S. Leite, “Machine Learning for Perovskites’ Reap-Rest-Recovery Cycle,” Joule, vol. 3, no. 2, pp. 325–337, 2019, doi: 10.1016/j.joule.2018.11.010.
 B. Yılmaz and R. Yıldırım, “Critical review of machine learning applications in perovskite solar research,” Nano Energy, vol. 80, p. 105546, 2021, doi: 10.1016/j.nanoen.2020.105546.
 F. Li et al., “Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells,” ENERGY Environ. Mater., vol. 2, no. 4, pp. 280–291, Dec. 2019, doi: 10.1002/eem2.12049.
 G. M. Tina, C. Ventura, S. Ferlito, and S. De Vito, “A state-of-art-review on machine-learning based methods for PV,” Appl. Sci., vol. 11, no. 16, 2021, doi: 10.3390/app11167550.
 Y. Feng, W. Hao, H. Li, N. Cui, D. Gong, and L. Gao, “Machine learning models to quantify and map daily global solar radiation and photovoltaic power,” Renew. Sustain. Energy Rev., vol. 118, no. September 2019, p. 109393, 2020, doi: 10.1016/j.rser.2019.109393.
 P. Kumari and D. Toshniwal, “Deep learning models for solar irradiance forecasting: A comprehensive review,” J. Clean. Prod., vol. 318, no. July, p. 128566, 2021, doi: 10.1016/j.jclepro.2021.128566.
 A. Sharma and A. Kakkar, “Forecasting daily global solar irradiance generation using machine learning,” Renew. Sustain. Energy Rev., vol. 82, no. August, pp. 2254–2269, 2018, doi: 10.1016/j.rser.2017.08.066.
 H. Michaels, I. Benesperi, and M. Freitag, “Challenges and prospects of ambient hybrid solar cell applications,” Chem. Sci., vol. 12, no. 14, pp. 5002–5015, 2021, doi: 10.1039/d0sc06477g.
 S. Manzhos, G. Giorgi, J. Lüder, and M. Ihara, “Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond,” Adv. Phys. X, vol. 6, no. 1, 2021, doi: 10.1080/23746149.2021.1908848.
 A. Lin and J. Phillips, “Optimization of random diffraction gratings in thin-film solar cells using genetic algorithms,” Sol. Energy Mater. Sol. Cells, vol. 92, no. 12, pp. 1689–1696, 2008, doi: 10.1016/j.solmat.2008.07.021.
 C. Forestiere, M. Donelli, G. F. Walsh, E. Zeni, G. Miano, and L. Dal Negro, “Particle-swarm optimization of broadband nanoplasmonic arrays,” Opt. Lett., vol. 35, no. 2, p. 133, 2010, doi: 10.1364/ol.35.000133.
 R. Rahmani et al., “Structure and Thickness Optimization of Active Layer in Nanoscale Organic Solar Cells,” Plasmonics, vol. 10, no. 3, pp. 495–502, Jun. 2015, doi: 10.1007/s11468-014-9833-x.
 A. A. Tabrizi, H. Saghaei, M. A. Mehranpour, and M. Jahangiri, “Enhancement of Absorption and Effectiveness of a Perovskite Thin-Film Solar Cell Embedded with Gold Nanospheres,” Plasmonics, vol. 16, no. 3, pp. 747–760, Jun. 2021, doi: 10.1007/s11468-020-01341-1.
 H. J. Snaith, “Perovskites: The Emergence of a New Era for Low-Cost, High-Efficiency Solar Cells,” J. Phys. Chem. Lett., vol. 4, no. 21, pp. 3623–3630, Nov. 2013, doi: 10.1021/jz4020162.
 H.-R. Jhong, D. S.-H. Wong, C.-C. Wan, Y.-Y. Wang, and T.-C. Wei, “A novel deep eutectic solvent-based ionic liquid used as electrolyte for dye-sensitized solar cells,” Electrochem. commun., vol. 11, no. 1, pp. 209–211, Jan. 2009, doi: 10.1016/j.elecom.2008.11.001.
 H. Tributsch, “Dye sensitization solar cells: A critical assessment of the learning curve,” Coord. Chem. Rev., vol. 248, no. 13–14, pp. 1511–1530, 2004, doi: 10.1016/j.ccr.2004.05.030.
 Z. Arifin, S. Soeparman, D. Widhiyanuriyawan, B. Sutanto, and Suyitno, “Performance enhancement of dye-sensitized solar cells (DSSCs) using a natural sensitizer,” Int. J. photoenergy, vol. 1788, 2017, doi: 10.1063/1.4968376.
 J. Burschka et al., “Sequential deposition as a route to high-performance perovskite-sensitized solar cells,” Nature, vol. 499, no. 7458, pp. 316–319, Jul. 2013, doi: 10.1038/nature12340.
 Y. Tang, X. Zeng, and J. Liang, “Surface plasmon resonance: An introduction to a surface spectroscopy technique,” J. Chem. Educ., vol. 87, no. 7, pp. 742–746, 2010, doi: 10.1021/ed100186y.
 A. Kojima, K. Teshima, Y. Shirai, and T. Miyasaka, “Organometal Halide Perovskites as Visible-Light Sensitizers for Photovoltaic Cells,” J. Am. Chem. Soc., vol. 131, no. 17, pp. 6050–6051, May 2009, doi: 10.1021/ja809598r.
 R. He, X. Huang, M. Chee, F. Hao, and P. Dong, “Carbon‐based perovskite solar cells: From single‐junction to modules,” Carbon Energy, vol. 1, no. 1, pp. 109–123, Sep. 2019, doi: 10.1002/cey2.11.
 J. Chun-Ren Ke et al., “In situ investigation of degradation at organometal halide perovskite surfaces by X-ray photoelectron spectroscopy at realistic water vapour pressure,” Chem. Commun., vol. 53, no. 37, pp. 5231–5234, 2017, doi: 10.1039/C7CC01538K.
 D. Bryant et al., “Light and oxygen induced degradation limits the operational stability of methylammonium lead triiodide perovskite solar cells,” Energy Environ. Sci., vol. 9, no. 5, pp. 1655–1660, 2016, doi: 10.1039/C6EE00409A.
 E. J. Juarez-Perez, Z. Hawash, S. R. Raga, L. K. Ono, and Y. Qi, “Thermal degradation of CH3NH3PbI3 perovskite into NH3 and CH3I gases observed by coupled thermogravimetry-mass spectrometry analysis,” Energy Environ. Sci., vol. 9, no. 11, pp. 3406–3410, 2016, doi: 10.1039/c6ee02016j.
 Y. Yuan et al., “Electric-Field-Driven Reversible Conversion Between Methylammonium Lead Triiodide Perovskites and Lead Iodide at Elevated Temperatures,” Adv. Energy Mater., vol. 6, no. 2, p. 1501803, Jan. 2016, doi: 10.1002/aenm.201501803.
 F. Matteocci et al., “Encapsulation for long-term stability enhancement of perovskite solar cells,” Nano Energy, vol. 30, no. July, pp. 162–172, Dec. 2016, doi: 10.1016/j.nanoen.2016.09.041.
 E. J. Juarez-Perez, L. K. Ono, M. Maeda, Y. Jiang, Z. Hawash, and Y. Qi, “Photodecomposition and thermal decomposition in methylammonium halide lead perovskites and inferred design principles to increase photovoltaic device stability,” J. Mater. Chem. A, vol. 6, no. 20, pp. 9604–9612, 2018, doi: 10.1039/c8ta03501f.
 N. Rolston et al., “Mechanical integrity of solution-processed perovskite solar cells,” Extrem. Mech. Lett., vol. 9, pp. 353–358, Dec. 2016, doi: 10.1016/j.eml.2016.06.006.
 N. R. Pochont and Y. R. Sekhar, “Numerical Simulation of Nitrogen-Doped Titanium Dioxide as an Inorganic Hole Transport Layer in Mixed Halide Perovskite Structures Using SCAPS 1-D,” Inorganics, vol. 11, no. 1, p. 3, Dec. 2022, doi: 10.3390/inorganics11010003.
 H. D. Pham, T. C. Yang, S. M. Jain, G. J. Wilson, and P. Sonar, “Development of Dopant‐Free Organic Hole Transporting Materials for Perovskite Solar Cells,” Adv. Energy Mater., vol. 10, no. 13, p. 1903326, Apr. 2020, doi: 10.1002/aenm.201903326.
 A. M. Shahrul et al., “Synergistic role of aluminium sulphate flocculation agent as bi-functional dye additive for Dye-Sensitized Solar Cell (DSSC),” Optik (Stuttg)., vol. 258, p. 168945, May 2022, doi: 10.1016/j.ijleo.2022.168945.
 B. O’Regan and M. Grätzel, “A low-cost, high-efficiency solar cell based on dye-sensitized colloidal TiO2 films,” Nature, vol. 353, no. 6346, pp. 737–740, Oct. 1991, doi: 10.1038/353737a0.
 J. Bisquert, “Dilemmas of Dye-Sensitized Solar Cells,” ChemPhysChem, vol. 12, no. 9, pp. 1633–1636, Jun. 2011, doi: 10.1002/cphc.201100248.
 F. Gao et al., “A new heteroleptic ruthenium sensitizer enhances the absorptivity of mesoporous titania film for a high efficiency dye-sensitized solar cell,” Chem. Commun., vol. 7345, no. 23, p. 2635, 2008, doi: 10.1039/b802909a.
 K. Zeng, Y. Chen, W.-H. Zhu, H. Tian, and Y. Xie, “Efficient Solar Cells Based on Concerted Companion Dyes Containing Two Complementary Components: An Alternative Approach for Cosensitization,” J. Am. Chem. Soc., vol. 142, no. 11, pp. 5154–5161, Mar. 2020, doi: 10.1021/jacs.9b12675.
 V. Kumar, R. Gupta, and A. Bansal, “Role of chenodeoxycholic acid as co-additive in improving the efficiency of DSSCs,” Sol. Energy, vol. 196, pp. 589–596, Jan. 2020, doi: 10.1016/j.solener.2019.12.034.
 S. James and R. Contractor, “Study on Nature-inspired Fractal Design-based Flexible Counter Electrodes for Dye-Sensitized Solar Cells Fabricated using Additive Manufacturing,” Sci. Rep., vol. 8, no. 1, p. 17032, Nov. 2018, doi: 10.1038/s41598-018-35388-2.
 R. Siavash Moakhar et al., “Recent Advances in Plasmonic Perovskite Solar Cells,” Adv. Sci., vol. 7, no. 13, p. 1902448, Jul. 2020, doi: 10.1002/advs.201902448.
 S. Zeng, D. Baillargeat, H. P. Ho, and K. T. Yong, “Nanomaterials enhanced surface plasmon resonance for biological and chemical sensing applications,” Chem. Soc. Rev., vol. 43, no. 10, pp. 3426–3452, 2014, doi: 10.1039/c3cs60479a.
 H. H. Nguyen, J. Park, S. Kang, and M. Kim, “Surface plasmon resonance: A versatile technique for biosensor applications,” Sensors (Switzerland), vol. 15, no. 5, pp. 10481–10510, 2015, doi: 10.3390/s150510481.
 K. Kneipp et al., “Single Molecule Detection Using Surface-Enhanced Raman Scattering (SERS),” Phys. Rev. Lett., vol. 78, no. 9, pp. 1667–1670, Mar. 1997, doi: 10.1103/PhysRevLett.78.1667.
 S. V Boriskina and L. Dal Negro, “Sensitive label-free biosensing using critical modes in aperiodic photonic structures,” Opt. Express, vol. 16, no. 17, p. 12511, Aug. 2008, doi: 10.1364/OE.16.012511.
 A. Polman, “Plasmonic Solar Cells,” in Advanced Photonics & Renewable Energy, 2010, vol. 16, no. 26, p. PWA2, doi: 10.1364/PV.2010.PWA2.
 L. Shi, Z. Zhou, and B. Tang, “Optik Optimization of Si solar cells with full band optical absorption increased in all polarizations using plasmonic backcontact grating,” Opt. - Int. J. Light Electron Opt., vol. 125, no. 2, pp. 789–794, 2014, doi: 10.1016/j.ijleo.2013.07.079.
 P. S. Chandrasekhar, P. K. Parashar, S. K. Swami, V. Dutta, and V. K. Komarala, “Enhancement of Y123 dye-sensitized solar cell performance using plasmonic gold nanorods,” Phys. Chem. Chem. Phys., vol. 20, no. 14, pp. 9651–9658, 2018, doi: 10.1039/c7cp08445e.
 L.-B. Luo et al., “Surface plasmon resonance enhanced highly efficient planar silicon solar cell,” Nano Energy, vol. 9, pp. 112–120, Oct. 2014, doi: 10.1016/j.nanoen.2014.07.003.
 S. Badillo et al., “An Introduction to Machine Learning,” Clin. Pharmacol. Ther., vol. 107, no. 4, pp. 871–885, 2020, doi: 10.1002/cpt.1796.
 A. L. Samuel, “Eight-move opening utilizing generalization learning. (See Appendix B, Game G-43.1 Some Studies in Machine Learning Using the Game of Checkers,” IBM J., pp. 210–229, 1959, doi: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5392560.
 R. Kohavi and F. Provost, “Glossary of Terms,” Mach. Learn., vol. 30, pp. 271–274, 1998, doi: https://doi.org/10.1023/A:1017181826899.
 W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, “Definitions, methods, and applications in interpretable machine learning,” Proc. Natl. Acad. Sci., vol. 116, no. 44, pp. 22071–22080, Oct. 2019, doi: 10.1073/pnas.1900654116.
 A. Navon, R. Machlev, D. Carmon, A. E. Onile, J. Belikov, and Y. Levron, “Effects of the COVID-19 pandemic on energy systems and electric power grids—A review of the challenges ahead,” Energies, vol. 14, no. 4, pp. 1–14, 2021, doi: 10.3390/en14041056.
 S. Katoch, S. S. Chauhan, and V. Kumar, “A review on genetic algorithm: past, present, and future,” Multimed. Tools Appl., vol. 80, no. 5, pp. 8091–8126, Feb. 2021, doi: 10.1007/s11042-020-10139-6.
 Q. Bai, “Analysis of Particle Swarm Optimization Algorithm,” Comput. Inf. Sci., vol. 3, no. 1, Jan. 2010, doi: 10.5539/cis.v3n1p180.
 D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft Comput., vol. 22, no. 2, pp. 387–408, Jan. 2018, doi: 10.1007/s00500-016-2474-6.
 Tin Kam Ho, “The random subspace method for constructing decision forests,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 832–844, 1998, doi: 10.1109/34.709601.
 O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, Nov. 2018, doi: 10.1016/j.heliyon.2018.e00938.
 D. Whitley, “A genetic algorithm tutorial,” Stat. Comput., vol. 4, no. 2, pp. 65–85, Jun. 1994, doi: 10.1007/BF00175354.
 H. N. Fakhouri, A. Hudaib, and A. Sleit, “Hybrid Particle Swarm Optimization with Sine Cosine Algorithm and Nelder–Mead Simplex for Solving Engineering Design Problems,” Arab. J. Sci. Eng., vol. 45, no. 4, pp. 3091–3109, Apr. 2020, doi: 10.1007/s13369-019-04285-9.
 Ç. Odabaşı Özer and R. Yıldırım, “Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools,” Nano Energy, vol. 56, pp. 770–791, 2019, doi: 10.1016/j.nanoen.2018.11.069.
 S. Lu, Q. Zhou, Y. Ouyang, Y. Guo, Q. Li, and J. Wang, “Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning,” Nat. Commun., no. 2018, pp. 1–8, doi: 10.1038/s41467-018-05761-w.
 Ç. Odabaşı and R. Yıldırım, “Machine learning analysis on stability of perovskite solar cells,” Sol. Energy Mater. Sol. Cells, vol. 205, no. November, p. 110284, 2020, doi: 10.1016/j.solmat.2019.110284.
 T. Chen, Y. Zhou, and M. Rafailovich, “Application of Machine Learning in Perovskite Solar Cell Crystal Size Distribution Analysis,” MRS Adv., vol. 4, no. 14, pp. 793–800, Mar. 2019, doi: 10.1557/adv.2019.145.
 Ç. Odabaşı and R. Yıldırım, “Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning,” Energy Technol., vol. 8, no. 12, pp. 1–12, 2020, doi: 10.1002/ente.201901449.
 J. Im, S. Lee, T.-W. Ko, H. W. Kim, Y. Hyon, and H. Chang, “Identifying Pb-free perovskites for solar cells by machine learning,” npj Comput. Mater., vol. 5, no. 1, p. 37, Mar. 2019, doi: 10.1038/s41524-019-0177-0.
 W. Sun et al., “Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials,” Sci. Adv., vol. 5, no. 11, pp. 1–9, 2019, doi: 10.1126/sciadv.aay4275.
 X. Zhai, M. Chen, and W. Lu, “Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods,” Comput. Mater. Sci., vol. 151, no. April, pp. 41–48, 2018, doi: 10.1016/j.commatsci.2018.04.031.
 R. Jinnouchi, J. Lahnsteiner, F. Karsai, G. Kresse, and M. Bokdam, “Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference,” Phys. Rev. Lett., vol. 122, no. 22, p. 225701, 2019, doi: 10.1103/PhysRevLett.122.225701.
 Z. Li, Q. Xu, Q. Sun, Z. Hou, and W. J. Yin, “Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning,” Adv. Funct. Mater., vol. 29, no. 9, pp. 1–9, 2019, doi: 10.1002/adfm.201807280.
 F. Azri, A. Meftah, N. Sengouga, and A. Meftah, “Electron and hole transport layers optimization by numerical simulation of a perovskite solar cell,” Sol. Energy, vol. 181, no. December 2018, pp. 372–378, 2019, doi: 10.1016/j.solener.2019.02.017.
 Y. Li et al., “Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning,” RSC Adv., vol. 11, no. 26, pp. 15688–15694, 2021, doi: 10.1039/D1RA03117A.
 Y. Wen, L. Fu, G. Li, J. Ma, and H. Ma, “Accelerated Discovery of Potential Organic Dyes for Dye-Sensitized Solar Cells by Interpretable Machine Learning Models and Virtual Screening,” Sol. RRL, vol. 4, no. 6, pp. 1–11, 2020, doi: 10.1002/solr.202000110.
 F. Wang, S. Langford, and H. Nakai, “Robust design of D-π-A model compounds using digital structures for organic DSSC applications,” J. Mol. Graph. Model., vol. 102, p. 107798, 2021, doi: 10.1016/j.jmgm.2020.107798.
 V. Venkatraman, “Evaluation of Molecular Fingerprints for Determining Dye Aggregation on Semiconductor Surfaces,” Mol. Inform., vol. 41, no. 1, p. 2000062, Jan. 2022, doi: 10.1002/minf.202000062.
 Q. Arooj and F. Wang, “Switching on optical properties of D-Π-A DSSC sensitizers from Π-spacers towards machine learning,” Sol. Energy, vol. 188, no. June, pp. 1189–1200, 2019, doi: 10.1016/j.solener.2019.06.044.
 S. S. Sutar, S. M. Patil, S. J. Kadam, R. K. Kamat, D. K. Kim, and T. D. Dongale, “Analysis and Prediction of Hydrothermally Synthesized ZnO-Based Dye-Sensitized Solar Cell Properties Using Statistical and Machine-Learning Techniques,” ACS Omega, vol. 6, no. 44, pp. 29982–29992, 2021, doi: 10.1021/acsomega.1c04521.
 Z. Li et al., “Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations,” J. Phys. Chem. A, vol. 122, no. 18, pp. 4571–4578, 2018, doi: 10.1021/acs.jpca.8b02842.
 H. Li et al., “A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells,” J. Comput. Chem., vol. 36, no. 14, pp. 1036–1046, 2015, doi: 10.1002/jcc.23886.
 P. R. Regonia, C. M. Pelicanoa, R. Tani, A. Ishizumi, H. Yanagi, and K. Ikeda, “Predicting the band gap of ZnO quantum dots via supervised machine learning models,” Optik (Stuttg)., vol. 207, 2020, doi: https://doi.org/10.1016/j.ijleo.2020.164469.
 Y. Zhang and X. Xu, “Machine Learning Band Gaps of Doped-TiO2Photocatalysts from Structural and Morphological Parameters,” ACS Omega, vol. 5, no. 25, pp. 15344–15352, 2020, doi: 10.1021/acsomega.0c01438.
 G. Aiello, S. Alfonzetti, S. A. Rizzo, and N. Salerno, “Optimization of plasmon-enhanced thin-film heterojunction solar cells,” IEEE Trans. Magn., vol. 51, no. 3, pp. 2–5, 2015, doi: 10.1109/TMAG.2014.2361272.
 S. C. Yen, Y. L. Chen, and Y. H. Su, “Materials genome evolution of surface plasmon resonance characteristics of Au nanoparticles decorated ZnO nanorods,” APL Mater., vol. 8, no. 9, 2020, doi: 10.1063/5.0023540.