International Journal of Emerging Research in Engineering, Science, and Management
Vol. 4, Issue 4, pp. 25-41, Oct-Dec 2025.
https://doi.org/10.58482/ijeresm.v4i4.4

Received: 14 Oct 2025 | Revised: 16 Dec 2025 | Accepted: 24 Dec 2025 | Published: 30 Dec 2025

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

Full Text PDF

Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors

Mohammed Saleh Alshaikh

Device Simulation Laboratory, Department of Electrical Engineering, College of Engineering and Architecture,
Umm Al-Qura University, Makkah, Saudi Arabia

Abstract: The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

Keywords: General Regression Neural Networks, Organic Solar Cells, Power Conversion Efficiency, Sensitivity Analysis, Support Vector Machine, Tree Boost.

References: 

  1. H. Sahu, W. Rao, A. Troisi, and H. Ma, “Toward predicting efficiency of organic solar cells via machine learning and improved descriptors,” Advanced Energy Materials, vol. 8, no. 24, Jul. 2018, doi: 10.1002/aenm.201801032.
  2. M. A. Green, S. R. Wenham, J. Zhao, J. Zolper and A. W. Blakers, “Recent improvements in silicon solar cells and module efficiency,” IEEE Conference on Photovoltaic Specialists, Kissimmee, FL, USA, 1990, pp. 207-210 vol.1, doi: 10.1109/PVSC.1990.111618.
  3. Mohd. R. S. Shaikh, “A Review Paper on Electricity Generation from Solar Energy,” International Journal for Research in Applied Science and Engineering Technology, vol. V, no. IX, pp. 1884–1889, Sept. 2017, doi: 10.22214/ijraset.2017.9272.
  4. S. M. Ho, “Power conversion efficiency in thin film solar cell: A review,” International Journal of Chemical Sciences, vol. 14, no. 1, pp. 143–151, 2016. https://www.tsijournals.com/abstract/power-conversion-efficiency-in-thin-film-solar-cell-a-review-10206.html
  5. S. Ho, E. E. Igbokwe, and O. O. Olasanmi, “Photovoltaic Technology: Power Conversion Efficiency of Solar Cells: A review,” Asian Journal of Chemistry, vol. 37, no. 9, pp. 2092–2114, Aug. 2025, doi: 10.14233/ajchem.2025.34134.
  6. S. M. Ho and I. Hassan, “Lead-free Perovskite Materials for Solar Cell: An Update of Recent Trends,” International Journal of Thin Film Science and Technology, vol. 11, no. 3, pp. 283–292, Sept. 2022, doi: 10.18576/ijtfst/110304.
  7. O. A. Abdulrazzaq, V. Saini, S. Bourdo, E. Dervishi, and A. S. Biris, “Organic Solar Cells: A review of materials, limitations, and possibilities for improvement,” Particulate Science and Technology, vol. 31, no. 5, pp. 427–442, Feb. 2013, doi: 10.1080/02726351.2013.769470.
  8. S. Dey, “Recent progress in molecular design of fused ring electron acceptors for organic solar cells,” Small, vol. 15, no. 21, p. e1900134, Apr. 2019, doi: 10.1002/smll.201900134.
  9. J. Hou, O. Inganäs, R. H. Friend, and F. Gao, “Organic solar cells based on non-fullerene acceptors,” Nature, Jan. 2018, doi: 10.1038/nmat5063.
  10. W. Zhao et al., “Molecular Optimization Enables over 13% Efficiency in Organic Solar Cells,” Journal of the American Chemical Society, vol. 139, no. 21, pp. 7148–7151, May 2017, doi: 10.1021/jacs.7b02677.
  11. D. Deng et al., “Fluorination-enabled optimal morphology leads to over 11% efficiency for inverted small-molecule organic solar cells,” Nature Communications, vol. 7, no. 1, p. 13740, Dec. 2016, doi: 10.1038/ncomms13740.
  12. J. Zhang, L. Zhu, and Z. Wei, “Toward over 15% power conversion efficiency for organic solar cells: Current status and Perspectives,” Small Methods, vol. 1, no. 12, Nov. 2017, doi: 10.1002/smtd.201700258.
  13. P. Cheng and X. Zhan, “Stability of organic solar cells: challenges and strategies,” Chemical Society Reviews, vol. 45, no. 9, pp. 2544–2582, Jan. 2016, doi: 10.1039/c5cs00593k.
  14. O. Ostroverkhova, “Organic Optoelectronic Materials: Mechanisms and applications,” Chemical Reviews, vol. 116, no. 22, pp. 13279–13412, Oct. 2016, doi: 10.1021/acs.chemrev.6b00127.
  15. A. Zhugayevych and S. Tretiak, “Theoretical description of structural and electronic properties of organic photovoltaic materials,” Annual Review of Physical Chemistry, vol. 66, no. 1, pp. 305–330, Jan. 2015, doi: 10.1146/annurev-physchem-040214-121440.
  16. A. S. Mahdi, L. M. Shaker, and A. Alamiery, “Recent advances in organic solar cells: materials, design, and performance,” Journal of Optics, vol. 53, no. 2, pp. 1403–1419, Jul. 2023, doi: 10.1007/s12596-023-01262-2.
  17. J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, Sep. 2017, doi: 10.1038/nature23474.
  18. D. Bzdok, M. Krzywinski, and N. Altman, “Machine learning: a primer,” Nature Methods, vol. 14, no. 12, pp. 1119–1120, Nov. 2017, doi: 10.1038/nmeth.4526.
  19. M. Krzywinski and N. Altman, “Classification and regression trees,” Nature Methods, vol. 14, no. 8, pp. 757–758, Jul. 2017, doi: 10.1038/nmeth.4370.
  20. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
  21. R. Jinnouchi and R. Asahi, “Predicting catalytic activity of nanoparticles by a DFT-Aided Machine-Learning algorithm,” The Journal of Physical Chemistry Letters, vol. 8, no. 17, pp. 4279–4283, Aug. 2017, doi: 10.1021/acs.jpclett.7b02010.
  22. L. Xu, T. Quan, J. Wang, T. A. Gulliver, and K. N. Le, “GR and BP neural network-based performance prediction of dual-antenna mobile communication networks,” Computer Networks, vol. 172, p. 107172, Feb. 2020, doi: 10.1016/j.comnet.2020.107172.
  23. D. Fooshee et al., “Deep learning for chemical reaction prediction,” Molecular Systems Design & Engineering, vol. 3, no. 3, pp. 442–452, Dec. 2017, doi: 10.1039/c7me00107j.
  24. C. W. Coley, R. Barzilay, T. S. Jaakkola, W. H. Green, and K. F. Jensen, “Prediction of organic reaction outcomes using machine learning,” ACS Central Science, vol. 3, no. 5, pp. 434–443, Apr. 2017, doi: 10.1021/acscentsci.7b00064.
  25. P. Sadowski, D. Fooshee, N. Subrahmanya, and P. Baldi, “Synergies between quantum mechanics and machine learning in reaction prediction,” Journal of Chemical Information and Modeling, vol. 56, no. 11, pp. 2125–2128, Oct. 2016, doi: 10.1021/acs.jcim.6b00351.
  26. J. P. Janet, L. Chan, and H. J. Kulik, “Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network,” The Journal of Physical Chemistry Letters, vol. 9, no. 5, pp. 1064–1071, Feb. 2018, doi: 10.1021/acs.jpclett.8b00170.
  27. J. D. Perea et al., “Combined computational approach based on density functional theory and artificial neural networks for predicting the solubility parameters of fullerenes,” The Journal of Physical Chemistry B, vol. 120, no. 19, pp. 4431–4438, Apr. 2016, doi: 10.1021/acs.jpcb.6b00787.
  28. R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi, and C. Kim, “Machine learning in materials informatics: recent applications and prospects,” Npj Computational Materials, vol. 3, no. 1, Dec. 2017, doi: 10.1038/s41524-017-0056-5.
  29. D. Xue, P. V. Balachandran, J. Hogden, J. Theiler, D. Xue, and T. Lookman, “Accelerated search for materials with targeted properties by adaptive design,” Nature Communications, vol. 7, no. 1, p. 11241, Apr. 2016, doi: 10.1038/ncomms11241.
  30. L. Ward, A. Agrawal, A. Choudhary, and C. Wolverton, “A general-purpose machine learning framework for predicting properties of inorganic materials,” Npj Computational Materials, vol. 2, no. 1, Aug. 2016, doi: 10.1038/npjcompumats.2016.28.
  31. D. F. Specht, “A general regression neural network,” in IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568-576, Nov. 1991, doi: 10.1109/72.97934.
  32. F. Y. Wu and K. K. Yen, “Applications of neural network in regression analysis,” Computers & Industrial Engineering, vol. 23, no. 1–4, pp. 93–95, Nov. 1992, doi: 10.1016/0360-8352(92)90071-q.
  33. C. Lopez-Martin, “Applying a general regression neural network for predicting development effort of short-scale programs,” Neural Computing and Applications, vol. 20, no. 3, pp. 389–401, Jun. 2010, doi: 10.1007/s00521-010-0405-5.
  34. R. Barzegar and A. A. Moghaddam, “Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction,” Modeling Earth Systems and Environment, vol. 2, no. 1, Jan. 2016, doi: 10.1007/s40808-015-0072-8.
  35. M. S. Elbisy, “Machine learning techniques for estimating wave-overtopping discharges at coastal structures,” Ocean Engineering, vol. 273, p. 113972, Feb. 2023, doi: 10.1016/j.oceaneng.2023.113972.
  36. M. M. Lotfinejad, R. Hafezi, M. Khanali, S. S. Hosseini, M. Mehrpooya, and S. Shamshirband, “A comparative assessment of predicting daily solar radiation using BAT Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) system: a case study,” Energies, vol. 11, no. 5, p. 1188, May 2018, doi: 10.3390/en11051188.
  37. V. Vapnik, S. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation and signal processing,” Advances in Neural Information Processing Systems, vol. 9, 1996. https://proceedings.neurips.cc/paper/1996/hash/4f284803bd0966cc24fa8683a34afc6e-Abstract.html
  38. J. H. Friedman and J. J. Meulman, “Multiple additive regression trees with application in epidemiology,” Statistics in Medicine, vol. 22, no. 9, pp. 1365–1381, Apr. 2003, doi: 10.1002/sim.1501.
  39. B. Efron and R. J. Tibshirani, An introduction to the Bootstrap. 1994. doi: 10.1201/9780429246593.
  40. G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, “Statistical learning,” in Springer texts in statistics, 2023, pp. 15–67. doi: 10.1007/978-3-031-38747-0_2.
Download PDF

This article is part of Volume 4, Issue 4 (Oct–Dec 2025)

© 2025 The Author(s). Published by IJERESM. This work is licensed under the Creative Commons Attribution 4.0 International License.

Archiving: All articles are permanently archived in Zenodo IJERESM Community.