Abstract

This study introduces an artificial intelligence framework for accelerating the discovery of stable, lead-free hybrid organic–inorganic double perovskites for solar energy applications. We combined a pre-trained Atomistic Line Graph Neural Network (ALIGNN) with gradient boosting ensembles to predict three critical properties: formation energy, bandgap, and Debye temperature. The ALIGNN model was trained on 8,000 crystal structures and achieved mean absolute errors of 0.011 eV per atom for formation energy, 0.094 eV for bandgap, and 10.5 K for Debye temperature. The gradient boosting models provided complementary accuracy and interpretability, particularly for bandgap classification. Using this pipeline, we screened 8,412 candidate compounds and identified K₂AgBiBr₆ as a promising material with a bandgap of 1.34 eV, a Debye temperature of 402 K, and a formation energy of −2.31 eV per atom. These values suggest long-term thermal stability and high photovoltaic potential without toxic lead. Compared with density functional theory calculations, our approach reduces computational cost by more than 90 percent while maintaining predictive fidelity. The framework offers a scalable path toward rapid identification of practical solar absorber materials and could significantly shorten the timeline for developing safe and efficient perovskite photovoltaics.

References

• International Renewable Energy Agency. (2021). World Energy Transitions Outlook 2021: 1.5°C Pathway. IRENA. https://www.irena.org/publications
• Green, M. A. (2006). Silicon photovoltaic cells: Advances and future prospects. Solar Energy, 76(1-3), 3–8. https://doi.org/10.1016/j.solener.2003.09.015
• Babayigit, A., Ethirajan, A., Muller, M., & Conings, B. (2016). Toxicity of lead-free perovskite solar cells. Nature Materials, 15(3), 247–251. https://doi.org/10.1038/nmat4572
• Kojima, A., Teshima, K., Shirai, Y., & Miyasaka, T. (2009). Organometal halide perovskites as visible-light sensitizers for photovoltaic cells. Journal of the American Chemical Society, 131(17), 6050–6051. https://doi.org/10.1021/ja809598r
• Best Research-Cell Efficiency Chart. (2023). National Renewable Energy Laboratory (NREL). https://www.nrel.gov/pv/cell-efficiency.html
• Noh, J. H., Im, S. H., Heo, J. H., Mandal, T. N., & Seok, S. I. (2013). Chemical management for colorful, efficient, and stable inorganic-organic hybrid nanostructured solar cells. Nano Letters, 13(4), 1764–1769. https://doi.org/10.1021/nl400349b
• Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2
• Xie, T., & Grossman, J. C. (2018). Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 120(14), 145301. https://doi.org/10.1103/PhysRevLett.120.145301
• Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: Recent applications and prospects. npj Computational Materials, 3(1), 54. https://doi.org/10.1038/s41524-017-0056-5
• Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., ... & Persson, K. A. (2013). The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. https://doi.org/10.1063/1.4812323
• Rupp, M. (2015). Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry, 115(16), 1058–1073. https://doi.org/10.1002/qua.24954
• Kresse, G., & Furthmüller, J. (1996). Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Physical Review B, 54(16), 11169. https://doi.org/10.1103/PhysRevB.54.11169
• Shluger, A. L., & Stoneham, A. M. (1993). Small polarons in real crystals: Concepts and problems. Journal of Physics: Condensed Matter, 5(19), 3049. https://doi.org/10.1088/0953-8984/5/19/003
• Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013). The high-throughput highway to computational materials design. Nature Materials, 12(3), 191–201. https://doi.org/10.1038/nmat3568
• Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., Ong, S. P., & Lu, W. (2020). Graph neural networks for scalable, accurate, and interpretable materials property prediction. npj Computational Materials, 6(1), 81. https://doi.org/10.1038/s41524-020-00362-3
• Sun, Y., Peng, H., Wang, J., & Yang, W. (2019). Machine learning for perovskite materials design and discovery. Journal of Materials Chemistry A, 7(45), 25639–25657. https://doi.org/10.1039/C9TA09051H
• Zhu, H., Liu, Y., Xu, K., Zhao, H., & Yu, D. (2021). Deep learning for material informatics: Methods, applications, and challenges. Advanced Intelligent Systems, 3(2), 2000145. https://doi.org/10.1002/aisy.202000145
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 30. https://arxiv.org/abs/1706.03762
• Zhu, Q., Zhang, L., Yang, W., & Wang, J. (2020). Application of convolutional neural networks in materials discovery and design. npj Computational Materials, 6(1), 85. https://doi.org/10.1038/s41524-020-00354-3
• Lee, J., Seko, A., Shitara, K., Nakayama, K., & Tanaka, I. (2016). Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques. Physical Review B, 93(11), 115104. https://doi.org/10.1103/PhysRevB.93.115104
• Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018). Inverse molecular design using machine learning: Generative models for matter engineering. Science, 361(6400), 360–365. https://doi.org/10.1126/science.aat2663
• Choudhary, K., Garrity, K. F., Reid, A. C. E., DeCost, B. L., Biacchi, A. J., Hight Walker, A. R., ... & Tavazza, F. (2021). The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Computational Materials, 7(1), 12. https://doi.org/10.1038/s41524-021-00542-2