EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE

Authors

Keywords:

Artificial intelligence, Machine learning, Perovskites, Phase engineering, HER
Dimensions

Abdullah, a., Abdulgafor, A., Yusuf, A., & Dahood, A. A. (2025). Improving the interpretability of ANN-Based Predictions of lattice constants in aliovalently doped perovskites using partial Dependence plot. Crystals, 15(6), 538. https://doi.org/doi:10.3390/cryst15060538

Alghadeer, M., Nufida, D. A., Mahmoud, h., Saad, M. A., Almer, A. B., & Fahhad, H. A. (2024). Machine learning prediction of materials properties from chemical composition: Status and prospects. Chemical Physics Reviews. https://doi.org/doi:10.1063/5.0235541

Ali, A., Park, H., Mall, R., Aissa, B., Sanvito, S., Belaidi, A., & El-Mellouhi, F. (2020). Machine learning accelerated recovery of the cubic structure in mixed-cation perovskite thin films. Chem. Mater, 32, 2998-3006.

Badini, S., Regomdi, S., & Pugliese, R. (2023). Unleasehing the power of Artificial Intelligence in Materials Design. Materials(Basel), 16(17). https://doi.org/10.3390/ma16175927

Baloni, M., Sharma, R. C., Singh, H., Singh, M. K., Kumar, A., Sati, P. C., . . . Thakur, V. N. (2022). Effect of Nd doping on structural, dielectric, magnatic and ferroelectric properties of 0.8BiFeO3-0.2PbTiO3 solid aolution. Journal of Alloy Compound.

Cao, C., Jinshuo, L., Yang, H., Zhang, L., & Yang, W. (2024). Mechanism investigation of A-site doping on modulating electronic band structure and photocatalytic performance towards CO2 reduction of LaFeO3 perovskite. 17(5), 3733-3744. https://doi.org/10.1007/s12274-023-6285-7

Chander, S., & Vijaya, P. (2021). 3-Unsupervised learning methods for data clustering. Artificial Intelligence in Data Mining, 41-46.

Chen, H., Covert, L., Lundberg, S., & Lee, S. (2023). Algoriths to estimate Shapley value feature attributions. 5(6). https://doi.org/10.1038/s42256-023-00657-x

Chen, Z., Pan, S., Wang, J., Min, Y., Chen, Y., & Xue, Q. (2024). Machine learning will revolutionize perovskite solar cel. The innovation, 5(3), 100602. https://doi.org/10.1016/j.xinn.2024.100602

Chen, Z., Wu, Y., Wang, X., Jin, W., & Zhu, C. (2015). Ferromagnatism and enhanced activity in Nd doped BiFeO3 nanopowders. journal of Materials Science: Materials in Electronics, 6, 9929-9940.

Dawa, T., & Sajjadi, B. (2024). Exploring the potential of perovskite structures for chemical looping technology: A state-of-the-art review. Fuel Processing Technology, 253. https://doi.org/10.10.1016/j.fuproc.2023.108022

Deng, Z., Fang, K., Gong, C., Yue, H., Zhang, H., Li, K., . . . Tay, H. E. (2025). Prediction of ABX3 perovskite formation energy using machine learning. Materials, 18(13), 2927. https://doi.org/10.3390/ma18132927

Doggalie, P., Teraoka, Y., Rayalu, S., & Labhsetwar, N. (2015). Effect of A-site substitution in perovskites: catalytic properties of PrMnO3 and Ba/K/Ce substituted PrMnO3 for CO and PM oxidation. 3(1), 420-428.: https://doi.org/10.1016/j.jece.2014.11.019

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. Computer Science, Philosophy. https://doi.org/10.48550arXiv.1702.08608

Feng, Y., Dai, J., Wang, M., Ding, W., Zhang, H., Xu, W., & Wan, J. (2025). Unraveling metastable perovskites oxides insights from structural engineering to synthesis paradigms. Microstructures, 5, 1-29. https://dx.doi.org/10.doi.org/10.20517

Hung, V. T., Zhenxue, D., & Mohammad, R. (2025). Data-driven explainable machine learning approaches for predicting hydrogen adsorbtion in porus crystalline materials. Journal of Alloys and Compounds, 1028, 180709. https://doi.org/10.1016/j.jallcom.2025.180709

Jacobs, R., Liu, J., Abernathy, H., & Morgan, D. (2024). Machine learning design of perovskite catalytic properties. Advanced Energy Materials, 14(12). https://doi.org/10.1002/aenm.202303684

Jayakrishishnan, B., Misra, S., & Sastry, P. (2023). Temperature-driven structural phase transitions in 0.05(Na0.50Bi0.5)TiO3-0.95NaNbO3 solid solution via amplitude mode analysis. A Letter Journal Exploring the Frontiers of Physics, 1-8. https://doi.org/10.1209/0295-5075/acedcb

Jess, A., Yang, R., & Hages, J. C. (2022). On the phase stability of chalcogenide perovskites. Chemistry of Materials, 34(15), 6894-6901. https://doi.org/10.1021/acs.chemmater.2c01289

Jha, D., Gupta, V., Liao, W., Choudhary, A., & Agrawal, A. (2022). Moving closer to experimental level materials property pridiction using AI. https://doi.org/10.1038/s41598-022-15816-0

Jianjian, Y., Guoxiang, Z., Yunzhe, W. Q., & Xiaozhi, W. (2023). Recent advances in phase-engineered photocatalysts: Classification and diversified application. Materials, 16(11), 1-17. https://doi.org/10.3390/ma16113980

Jianqiao, L., Liqian, L., Boru, S., Di, W., Yuequ, Z., Jianzhao, W., & Ce, F. (2024). Transformative strategies in photocatalyst design: merging computational methods and deep learning. Material Informatics, 4, 2-33. http://dx.doi.org/10.20517/jml.2024.48

Kahlaoui, S., Belhorma, B., Labrim, H., Boujnah, M., & Regragui, M. (2024). Strain effects on the electronic, optical and electrical properties of Cu2ZnSnS4: DFT study. Heliyon, 6(4). https://doi.org/10.1016/j.heliyon.2020.e03713

Liu, H., Zhang, Y., & Chen, Z. (2024). Perovskite-based photocatalysts for hydrogen evolution: Recent advances and future perspectives. Renewable & Sustainable Energy Reviews, 150.

Luhan, D., Yulong, F., Mengran, W. F., Bailin, T., Guoqiang, W., Shuhua, L., & Mengning, D. (2024). Harnessing Electro-Descriptors for machine learning analysisi of photocatalytic organic reaction. American Chemical Society, 19019-19029.

Maciejewska, K., Szklarz, P., Bednarkiewicz, A., Dramicanin, M., & Marciniak, L. (2023). Thermally-induced structural phase transition in rare earth orthophosphate nanocrystals for highly sensitive thermal history paints. Journal of alloys and compounds, 935(1). https://doi.org/10.1016/j.jallcom.2022.168064

Nyangiwe, N. N. (2025). Applications of density functional theory and machine learning in nanomaterials: A review. Next Material, 8. https://doi.org/j,nxmate.2025.100683

Raccuglia, P., Elbert, K. C., & Adler, P. D. (2016). Machine-learning-assisted material discovery using failed experiments. 73-76.

Ren, Y., Zhao, X., & Li, M. (2023). Temperature-driven phase transitions in perovskite photocatalysts and their effect on hydrogen evolution. Applied Catalysis B: Environmental, 312.

Rui, D., Junhong, C., Yuxin, C., Jianguo, L., Yoshio, B., & Xuebin, W. (2024). Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation. Royal Society of Chemistry, 53, 11390-11461. https://doi.org/10.1039/D4CS00844H

Schmidt, J., Marques, M. R., Botti, S., & Marques, M. A. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5(83).

Shah, Z. H., Ge, Y., Ye, W., Lin, X., Zhang, S., & Lu, R. (2017). Visible activation of SrTiO3 by loading Ag/AgX (X=Cl, Br) for high efficient plasmon-enhanced photocatalysis. Material Chemistry and Physics, 73-82. https://doi.org/10.1016/j.matchemphys.2017.05.002

Tao, Q., Tian, L., Ye, S., Long, L., Wencong, L., & Minjie, L. (2021). Machine learning aided design of perovskite oxide materials for photocatalytic water splitting. Journal of Energy Chemistry, 351-359.

Temerov, F., Baghdadi, Y., Rattner, E., & Eslava, S. (2022). A review on Halid perovskite-based Photocatalysts: key factors and challenges. ACS Appl. Energy Materials, 14605-14637.

Toriqul, I. A., Saiduzzaman, M., Khandaker, M. H., Ismail, K. S., Mohammad, N. H., Sohail, A., & Mitro, S. (2024). Pressure-driven modification of optoelectronic features of ACaCl3 (A = Cs, Ti) for device applications. Heliyon, 10(5). https://doi.org/10.1016/j.heliyon.2024.e26733

Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., . . . Jain, A. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571(7763), 95-98.

Wang, M., Ni, Z., Xiao, X., Zhou, Y., & Huang, J. (2025). Strain engineering in metal halide perovskite materials and devices: Influence on Stability and optoelectronic properties. Chemical Physics Reviews, 1-16.

Wayo, D. D., & Goliatt, L. G. (2024). AI and quantum computing in binary photocatalytic hydrogen production. 1-31. https://arxiv.org/abs/2501.00575

Weng, B. C., Song, Z. L., Zhu, R. L., Yan, Q. Y., Sun, Q. D., Grice, C. G., . . . Yin, W. J. (2020). Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts. Nature Communications, 11(1), 3513.

xiao, H., Pengyum, L., Ran, R., Wei, W., Wei, Z., & Zongping, S. (2022). Non-metal Florine dOPING in Ruddlesden-Popper perovskite oxide enables high-efficiency photocatalytic water splitting for hydrogen production. Material Today Energy, 23, 1-7. https://doi.org/10.1016/j.mtener.2021.100896

Yang, Z., Liu, Y., Zhang, Y., wang, L., Lin, C., Lv, Y., Shao, C. (2021). Machine learning Accelerates the discovery of ligth absorbing materials for double perovskite solar cells. journal of Physical Chemistry C, 22483-22492.

Zhang, X., Turiansky, M., Shen, J., Chris, G., & Walle, C. (2020). Iodine interstitials as a cause of non-radiative recombination in hybrid perovskites. Physical Review B, 140101.

Zhang, Z., Zhou, R., Li, D., Jiang, Y., Wang, X., Tang, H., & Xu, J. (2022). Recent progress in halide perovskite nanocrystals for photocatalytic hydrogen evolution. Nanomaterials, 13(1). https://doi.org/10.3390/nano13010106

Zhuo, Y., Tehrani, A. M., & Brgoch, J. (2018). Predicting the band gaps of inorganic solid by machine learning. Physical chemistry Letters, 1668-1673. https://doi.org/10.1021/acs.jpclett.8b00124

Published

14-08-2025

How to Cite

EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE. (2025). FUDMA JOURNAL OF SCIENCES, 9(8), 93-98. https://doi.org/10.33003/fjs-2025-0908-3894

How to Cite

EMERGING STRATEGIES IN PHASE-TUNED PEROVSKITE MATERIALS FOR EFFICIENT HYDROGEN EVOLUTION: A MACHINE LEARNING PERSPECTIVE. (2025). FUDMA JOURNAL OF SCIENCES, 9(8), 93-98. https://doi.org/10.33003/fjs-2025-0908-3894