Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning
Open accessAbstract
A dataset containing 599 data points from 146 publications on 2D/3D perovskite solar cells is analyzed using machine learning. The predictive models are developed for power conversion efficiency (PCE) using eXtreme Gradient Boosting regression, random forest regression and artificial neural networks while association rule mining is used to analyze the stability data to identify the descriptors leading to high stability 2D/3D cells. A predictive model is also developed for the bandgap to predict the missing values in the dataset for the use in PCE predictions. Models for both bandgap and PCE predictions are quite successful. The thickness of inorganic layer ( n ), radius of anion ( R x ), and 2D cation ( R m ) are found to be the most important descriptors for bandgap predictions; n and R m , together with the bandgap, are found to be deterministic for PCE in regular cells while the bandgap, n , and conduction band energy of hole transport layer are the most influential descriptors in inverted structures. Association rule mining analysis for the stability indicates that the cells with layered perovskite structures are more stable while the 2D and 3D cations leading to the most stable cells are found to be butylammonium and formamidinium‐Cs mixed cation respectively.
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Sorted by citations
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174 · see citing works
Bieńkowski, K., Solarska, R. A., Trinh, L., Widera-Kalinowska, J., Al‐Anesi, B., Liu, M., Grandhi, G. K., Vivo, P., Oral, B., Yılmaz, B., et al. (2024) · ACS Catalysis · doi:10.1021/acscatal.3c06040 · Cited by 91 · see citing works
Self-citationDatta, S., Baul, A., Sarker, G. C., Sadhu, P. K., & Hodges, D. R. (2023) · IEEE Access · doi:10.1109/access.2023.3298542 · Cited by 81 · see citing works
de la Asunción‐Nadal, V., Sprague, C. I., Guijarro‐Berdiñas, B., Cappel, U. B., & García‐Fernández, A. (2025) · EES solar. · doi:10.1039/d5el00041f · Cited by 48 · see citing works
The role of machine learning in perovskite solar cell research
Chen, C., Maqsood, A., & Jacobsson, T. J. (2023) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2023.170824 · Cited by 42 · see citing works
Predicting the bandgap and efficiency of perovskite solar cells using machine learning methods
Khan, A., Kandel, J., Tayara, H., & Chong, K. T. (2023) · Molecular Informatics · doi:10.1002/minf.202300217 · Cited by 33 · see citing works
Xu, Z., Chin, S., Park, B., Meng, Y., Kim, S., Han, S., Li, Y., Kim, D., Kim, B.-S., Lee, J., et al. (2024) · Next Materials · doi:10.1016/j.nxmate.2023.100103 · Cited by 32 · see citing works
Bhatt, P., Pandey, A. K., Rajput, A., Sharma, K. K., Moyez, S. A., & Tewari, A. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1677 · Cited by 25 · see citing works
Zhao, S., Zhou, S., Guo, Z., Luo, H., Jiang, Z., Lin, N., Chen, M., Li, L., & Li, C. (2025) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c01361 · Cited by 20 · see citing works
Zhang, Q., Wang, J., & Liu, G. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.07.018 · Cited by 18 · see citing works
Machine learning for a sustainable energy future
Oral, B., Çoşgun, A., Kilic, A., Eroğlu, D., Günay, M. E., & Yıldırım, R. (2024) · Chemical Communications · doi:10.1039/d4cc05148c · Cited by 16 · see citing works
Self-citationOral, B., Maddah, H. A., & Yıldırım, R. (2024) · Solar RRL · doi:10.1002/solr.202400432 · Cited by 16 · see citing works
Self-citationBandgap Prediction of Hybrid Organic–Inorganic Perovskite Solar Cell Using Machine Learning
Sadhu, D., De, D., Dattatreya, D., Deo, A., & Gupta, S. (2023) · Journal of The Institution of Engineers (India) Series D · doi:10.1007/s40033-023-00553-z · Cited by 15 · see citing works
Guo, C., Li, Z., Wang, K., Zhou, X., Huang, D., Liang, J., & Zhao, L. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp02368g · Cited by 14 · see citing works
Stanton, R., & Trivedi, D. J. (2023) · Nano Letters · doi:10.1021/acs.nanolett.3c04054 · Cited by 14 · see citing works
ML@ChemE: Past, Present, and Future of Machine Learning in Chemical Engineering
Özdemir, P., & Yıldırım, R. (2025) · ChemBioEng Reviews · doi:10.1002/cben.70012 · Cited by 14 · see citing works
Self-citationMachine‐Learning Accelerating the Development of Perovskite Photovoltaics
Liu, T., Wang, S., Shi, Y., Wu, L., Zhu, R., Wang, Y., Zhou, J., & Choy, W. C. H. (2023) · Solar RRL · doi:10.1002/solr.202300650 · Cited by 14 · see citing works
Lavoipierre, R., Planès, É., Flandin, L., & Perrin, L. (2024) · Chemistry of Materials · doi:10.1021/acs.chemmater.4c02217 · Cited by 11 · see citing works
Freitas, A. L. M., & Souza, J. A. (2023) · Journal of Materials Chemistry C · doi:10.1039/d3tc00593c · Cited by 10 · see citing works
Kuchi, C., Kalla, R. M. N., Kumar, Y. A., Al-Sehemi, A. G., Ravindran, E., & Chilaka, S. K. (2026) · Journal of Materials Science · doi:10.1007/s10853-025-11886-3 · Cited by 9 · see citing works
Li, H., Dixit, A., Kumari, A., Abraham, J. A., Mishra, A. K., Sharma, R., Kumari, P., & Kallel, M. (2025) · Journal of Inorganic and Organometallic Polymers and Materials · doi:10.1007/s10904-025-03814-4 · Cited by 8 · see citing works
Stanton, R., Gupta, S. K., & Trivedi, D. J. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c05842 · Cited by 7 · see citing works
Yılmaz, B., Ceylan, H. B., Yaz, G., & Yıldırım, R. (2025) · Energy & Fuels · doi:10.1021/acs.energyfuels.5c00792 · Cited by 6 · see citing works
Self-citationAlgorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells
Oh, D., Kim, S., Perini, C. A. R., Correa‐Baena, J., & Sahinidis, N. V. (2025) · ACS Energy Letters · doi:10.1021/acsenergylett.5c02477 · Cited by 4 · see citing works
Yang, X., Yang, Y., Meng, H., Li, Y., & Hu, Q. (2024) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.4c01715 · Cited by 2 · see citing works
Stanton, R., & Trivedi, D. J. (2024) · ACS Applied Energy Materials · doi:10.1021/acsaem.3c03276 · Cited by 1 · see citing works
Zhang, L.-D., & Feng, H. (2025) · Journal of Materials Chemistry A · doi:10.1039/d5ta00579e · Cited by 1 · see citing works
Wang, X.-D., Chen, Z.-R., Li, W.-K., Guo, P., Mu, C., Gao, Z.-F., & Lu, Z.-Y. (2026) · Molecules · doi:10.3390/molecules31030440 · Cited by 1 · see citing works
Jianwei, G. O. N. G. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.19387
Towards system-level artificial intelligence in perovskite photovoltaics
Li, Y., Liu, W., Cao, Q., Di, S., Nazeeruddin, M. K., & Li, G. (2026) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-026-00332-4
- Authors
- Yılmaz, B., Odabaşı, Ç., & Yıldırım, R.
- Affiliations
- Boğaziçi University (TR)
- Corresponding author
- Yıldırım, Ramazan
- Venue
- Energy Technology
- Publisher
- Wiley
- Published
- 2022-01-27
- Volume
- 10
- Issue
- 3
- ISSN
- 2194-4288 (ISSN-L)
- 2194-4296
- OpenAlex
- W4205401561
- Cited by
- 30
- References
- 56
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 7 · 2023: 10 · 2022: 2
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Energy Technology (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › 2D Materials and Applications
- Keywords
- power conversion efficiency prediction, device stability, band gap prediction, machine learning descriptors, association rule mining, hole transport layer, mixed-cation perovskites, butylammonium, formamidinium, layered perovskite structure
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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