MXene-Integrated Perovskite Tandem Solar Cells for Sustainable Development Goals (SDGs): A Bibliometric and Physics-Informed Artificial Intelligence (AI) Analysis Using Graph Neural Networks and Transformer Models
), Fatima Hassan Mohammed(2), Khadersab Adamsab(3),
(1) Prince Sattam bin Abdulaziz University
(2) University of Khartoum
(3) University of Technology and Applied Sciences–Al Musannah
Corresponding Author
Abstract
Keywords
References
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., and Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
Al-Ashouri, A., Köhnen, E., Li, B., Magomedov, A., Hempel, H., Caprioglio, P., Márquez, J. A., Morales Vilches, A. B., Kasparavicius, E., Smith, J. A., Phung, N., Menzel, D., Grischek, M., Kegelmann, L., Skroblin, D., Gollwitzer, C., Malinauskas, T., Jošt, M., Matič, G., Rech, B., Schlatmann, R., Topič, M., Korte, L., Abate, A., Stannowski, B., Neher, D., Stolterfoht, M., Unold, T., Getautis, V., and Albrecht, S. (2020). Monolithic perovskite/silicon tandem solar cell with >29% efficiency by enhanced hole extraction. Science, 370(6522), 1300–1309.
Aydin, E., Allen, T. G., De Bastiani, M., Razzaq, A., Xu, L., Ugur, E., Liu, J., and De Wolf, S. (2024). Pathways toward commercial perovskite/silicon tandem photovoltaics. Science, 383(6679), eadh3849.
Chin, X. Y., Turkay, D., Steele, J. A., Tabean, S., Eswara, S., Mensi, M., Fiala, P., Wolff, C. M., Paracchino, A., Artuk, K., Jacobs, D., Guesnay, Q., Sahli, F., Andreatta, G., Boccard, M., Jeangros, Q., and Ballif, C. (2023). Interface passivation for 31.25%-efficient perovskite/silicon tandem solar cells. Science, 381(6653), 59–63.
Choudhary, K., and DeCost, B. (2021). Atomistic line graph neural network for improved materials property predictions. NPJ Computational Materials, 7(1), 185.
Correa-Baena, J. P., Saliba, M., Buonassisi, T., Grätzel, M., Abate, A., Tress, W., and Hagfeldt, A. (2017). Promises and challenges of perovskite solar cells. Science, 358(6364), 739–744.
Dale, P. J., and Scarpulla, M. A. (2023). Efficiency versus effort: A better way to compare best photovoltaic research cell efficiencies? Solar Energy Materials and Solar Cells, 251, 112097.
de la Asunción-Nadal, V., Palomares, E., and García-Belmonte, G. (2025). Machine learning assisted strategies for next-generation perovskite photovoltaics. Advanced Energy Materials, 15(3), 2401456.
Dizayee, W., Marhoon, I. I., Mohammed, M. A., Zorah, M., Al-Husseini, Z. S. M., Abdulnabi, M. S., Abdulkareem-Alsultan, G. and Nassar, M. F. (2025). Glycine-functionalized Ti₃C₂Tx MXene with improved material properties for concurrent Sn²⁺ oxidation mitigation and defect passivation in efficient tin halide perovskite solar cells. Journal of Science: Advanced Materials and Devices, 11(1), 101085. Grätzel, M. (2014). The light and shade of perovskite solar cells. Nature Materials, 13(9), 838–842.
Green, M. A., Dunlop, E. D., Yoshita, M., Kopidakis, N., Bothe, K., Siefer, G., Hao, X., and Jiang, J. Y. (2024). Solar cell efficiency tables (Version 65). Progress in Photovoltaics: Research and Applications, 32(7), 3-15.
Heo, J. H., Zhang, F., Park, J. K., Lee, H. J., Lee, D. S., Heo, S. J., Luther, J. M., Berry, J. J., Zhu, K., and Im, S. H. (2022). Surface engineering with oxidized Ti₃C₂Tx MXene enables efficient and stable pin-structured CsPbI₃ perovskite solar cells. Joule, 6(7), 1672–1688.
Hou, Y., Aydin, E., De Bastiani, M., Xiao, C., Isikgor, F. H., Xue, D. J., Chen, B., Chen, H., Bahrami, B., Chowdhury, A. H., Johnston, A., Baek, S. W., Huang, Z., Wei, M., Dong, Y., Troughton, J., Jalmood, R., Mirabelli, A. J., Allen, T. G., Van Kerschaver, E., Saidaminov, M.I., Baran, D., Qiao, Q., Zhu, K., De Wolf, S., and Sargent, E. H. (2020). Efficient tandem solar cells with solution-processed perovskite on textured crystalline silicon. Science, 367(6482), 1135–1140.
Hui, X., Ge, X., Zhao, R., Li, Z., and Yin, L. (2020). Interface chemistry on MXene-based materials for enhanced energy storage and conversion performance. Advanced Functional Materials, 30(50), 2005190.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L. (2021). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422–440.
Kojima, A., Teshima, K., Shirai, Y., and Miyasaka, T. (2009). Organometal halide perovskites as visible-light sensitizers for photovoltaic cells. Journal of the American Chemical Society, 131(17), 6050–6051.
Li, X., Bai, Y., Shi, X., Su, N., Nie, G., Zhang, R., and Ye, L. (2021). Applications of MXene (Ti₃C₂Tx) in photocatalysis: A review. Materials Advances, 2(5), 1570–1594.
Lim, B., Arık, S. Ö., Loeff, N., and Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748–1764.
Mousavi, R., Mousavi, A., Mousavi, Y., Tavasoli, M., Arab, A., Kucukdemiral, I. B., Alfi, A., and Fekih, A. (2025). Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency. Applied Energy, 382, 125296.
Niu, G., Guo, X., and Wang, L. (2015). Review of recent progress in chemical stability of perovskite solar cells. Journal of Materials Chemistry A, 3(17), 8970–8980.
Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.
Schütt, K. T., Sauceda, H. E., Kindermans, P. J., Tkatchenko, A., and Müller, K. R. (2018). SchNet: A deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24), 241722.
Shah, S. A. A., Sayyad, M. H., Khan, K., Sun, J., and Guo, Z. (2021). Application of MXenes in perovskite solar cells: A short review. Nanomaterials, 11, 2151.
Shahzad, F., Alhabeb, M., Hatter, C. B., Anasori, B., Hong, S. M., Koo, C. M., and Gogotsi, Y. (2016). Electromagnetic interference shielding with 2D transition metal carbides (MXenes). Science, 353(6304), 1137–1140.
Snaith, H. J. (2018). Present status and future prospects of perovskite photovoltaics. Nature Materials, 17(5), 372–376.
Thakur, A., Zhang, Y., Gogotsi, Y., and Anasori, B. (2025). Electrochemistry of MXenes and their sustainable energy applications. MRS Energy and Sustainability, 12(2), 270–282.
Torlao, V., and Fajardo, E. A. (2025). Formation energy prediction of material crystal structures using deep learning. Materials Research Express, 12(12), 125501.
Wang, D., Wright, M., Elumalai, N. K., and Uddin, A. (2016). Stability of perovskite solar cells. Solar Energy Materials and Solar Cells, 147, 255–275.
Wang, Y., Chen, X., Li, J., and Zhang, L. (2022). Deep learning-enabled intelligent fault diagnosis and performance prediction in photovoltaic systems. Renewable Energy, 189, 1250–1264.
Xie, T., and Grossman, J. C. (2018). Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 120(14), 145301.
Article Metrics
Abstract View
: 0 times
Download : 0 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Bumi Publikasi Nusantara

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.








