Machine Learning Applications in Materials Modeling and Optimization for Sustainable Energy Storage: A Systematic Approach
Main Article Content
Abstract
The study demonstrates that machine learning is becoming increasingly important in the modelling and optimisation of materials for sustainable energy storage. This field is growing rapidly, as evidenced by the diversity of applied techniques and the complexity of the materials analysed. The paper identifies the need for models capable of integrating multiple variables and scales, as well as the importance of advancing methods that overcome current limitations in accuracy, generalisation, and data availability. The text also underscores the significance of interdisciplinary approaches that integrate theory, experimentation, and industrial application to expedite the development of efficient and reliable solutions. The enhancement of the impact of machine learning on the design and improvement of materials is contingent on multisector collaboration and standardisation in data management. Such collaboration and standardisation are pivotal in ensuring the sustainability and competitiveness of future energy systems.
Article Details
Section
How to Cite
References
1. D. A. Bin Abu Sofian, H. R. Lim, H. Siti Halimatul Munawaroh, Z. Ma, K. W. Chew, and P. L. Show, “Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage,” Sustainable Development, vol. 32, no. 4, pp. 3953–3978, 2024.
2. Sharma, P. K. Singh, E. Makki, J. Giri, and T. Sathish, “A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior,” Heliyon, vol. 10, no. 3, 2024.
3. R. Punyavathi, A. Pandian, A. R. Singh, M. Bajaj, M. B. Tuka, and V. Blazek, “Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control,” Sci Rep, vol. 14, no. 1, p. 5661, 2024.
4. H. Alghamdi et al., “Latest Advancements in Solar Photovoltaic‐Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses,” Int J Energy Res, vol. 2024, no. 1, p. 1050785, 2024.
5. M. A. Adewoyin, O. Adediwin, and A. J. Audu, “Artificial intelligence and sustainable energy development: A review of applications, challenges, and future directions,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, no. 2, pp. 196–203, 2025.
6. D. H. Adebayo et al., “Optimizing energy storage for electric grids: Advances in hybrid technologies,” management, vol. 10, p. 11, 2025.
7. S. M. Y. Bhuiyan, A. Chowdhury, M. S. Hossain, S. M. Mobin, and I. Parvez, “AI-Driven Optimization in Renewable Hydrogen Production,” A Review. American Journal of Interdisciplinary Studies, vol. 6, no. 1, pp. 76–94, 2025.
a. Gulraiz et al., “Energy Advancements and Integration Strategies in Hydrogen and Battery Storage for Renewable Energy Systems. iScience,” 2025.
8. M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” bmj, vol. 372, p. 10, 2021, [Online]. Available: https://doi.
9. T. Asubiaro, S. Onaolapo, and D. Mills, “Regional disparities in Web of Science and Scopus journal coverage,” Scientometrics, vol. 129, no. 3, pp. 1469–1491, 2024, [Online]. Available: https://doi.
10. X. Liu et al., “A Fast Forward Prediction Marco for Energy Materials Design Based on Machine Learning Methods,” Energy Material Advances, vol. 5, p. 131, 2024.
11. L. Zhao, Y. Chang, S. Qiu, H. Liu, J. Zhao, and J. Gao, “High mechanical energy storage capacity of ultranarrow carbon nanowires bundles by machine learning driving predictions,” Advanced Energy and Sustainability Research, vol. 4, no. 11, p. 2300112, 2023.
12. S. Sun et al., “Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis,” Joule, vol. 3, no. 6, pp. 1437–1451, 2019.
13. M. T. Nguyen, R. Rousseau, P. D. Paviet, and V. A. Glezakou, “Actinide molten salts: a machine-learning potential molecular dynamics study,” ACS Appl Mater Interfaces, vol. 13, no. 45, pp. 53398–53408, 2021.
14. M. Babar, H. L. Parks, G. Houchins, and V. Viswanathan, “An accurate machine learning calculator for the lithium-graphite system,” Journal of Physics: Energy, vol. 3, no. 1, p. 14005, 2020.
15. X. Huang et al., “Applying machine learning to balance performance and stability of high energy density materials,” iScience, vol. 24, no. 3, 2021.
16. P. Patel and S. P. Ong, “Artificial intelligence is aiding the search for energy materials,” MRS Bull, vol. 44, no. 3, pp. 162–163, 2019.
17. Q. Deng and B. Lin, “Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage,” Energy Mater, vol. 1, p. 100006, 2021.
18. Q. Bai, Y. Duan, J. Lian, and X. Wang, “Computation-accelerated discovery of the K2NiF4-type oxyhydrides combing density functional theory and machine learning approach,” Front Chem, vol. 10, p. 964953, 2022.
19. Q. Gou et al., “Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials,” iScience, vol. 27, no. 4, 2024.
20. N. H. Paulson, J. A. Libera, and M. Stan, “Flame spray pyrolysis optimization via statistics and machine learning,” Mater Des, vol. 196, p. 108972, 2020.
21. M. Rajabi, J. M. Sardroud, and A. Kheyroddin, “Green standard model using machine learning: identifying threats and opportunities facing the implementation of green building in Iran,” Environmental Science and Pollution Research, vol. 28, pp. 62796–62808, 2021.
a. V. L. N. Sujith et al., “Integrating Nanomaterial and High‐Performance Fuzzy‐Based Machine Learning Approach for Green Energy Conversion,” J Nanomater, vol. 2022, no. 1, p. 5793978, 2022.
22. H. Wang, X. L. Pan, Y. F. Wang, X. R. Chen, Y. X. Wang, and H. Y. Geng, “Lattice dynamics and elastic properties of α-U at high-temperature and high-pressure by machine learning potential simulations,” Journal of Nuclear Materials, vol. 572, p. 154029, 2022.
23. G. Trezza and E. Chiavazzo, “Leveraging composition-based energy material descriptors for machine learning models,” Mater Today Commun, vol. 36, p. 106579, 2023.
24. J. Li et al., “Machine learning assisted prediction in the discharge capacities of novel MXene cathodes for aluminum ion batteries,” J Energy Storage, vol. 82, p. 110196, 2024.
25. Hemavathi, G. Vidya, and A. KS, “Machine learning in the era of smart automation for renewable energy materials,” e-Prime-Advances in Electrical Engineering, Electronics and Energy, vol. 7, p. 100458, 2024.
26. H. Tian, W. Dong, W. Zhang, and C. Guo, “Machine learning techniques to probe the properties of molten salt phase change materials for thermal energy storage,” Cell Rep Phys Sci, vol. 5, no. 7, 2024.
27. L. Wu, T. Guo, and T. Li, “Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts,” iScience, vol. 24, no. 5, 2021.
28. T. Morawietz and N. Artrith, “Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications,” J Comput Aided Mol Des, vol. 35, no. 4, pp. 557–586, 2021.
29. H. Liu, Z. Cui, Z. Qiao, X. An, and Y. Wang, “Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors,” Journal of Materials Informatics, vol. 4, no. 4, p., 2024.
30. López et al., “Machine‐Learning Aided First‐Principles Prediction of Earth‐Abundant Pnictogen Chalcohalide Solid Solutions for Solar‐Cell Devices,” Adv Funct Mater, vol. 34, no. 42, p. 2406678, 2024.
31. T. Biswas, A. Gupta, and A. K. Singh, “Many-body physics and machine learning enabled discovery of promising solar materials,” RSC Adv, vol. 15, no. 11, pp. 8253–8261, 2025.
a. Verma and K. Joshi, “MH-PCTpro: A machine learning model for rapid prediction of pressure-composition-temperature (PCT) isotherms,” iScience, vol. 28, no. 4, 2025.
32. Ifandi et al., “Noble metal catalyst detection in rocks using machine-learning: The future to low-cost, green energy materials?,” Sci Rep, vol. 13, no. 1, p. 3765, 2023.
33. Z. He and H. Zhang, “Retracted] Phase Prediction Study of High‐Entropy Energy Alloy Generation Based on Machine Learning,” Comput Intell Neurosci, vol. 2022, no. 1, p. 8904341, 2022.
34. J. Xu et al., “Superior polymeric gas separation membrane designed by explainable graph machine learning,” Cell Rep Phys Sci, vol. 5, no. 7, 2024.
35. Z. Chen, F. C. Bononi, C. A. Sievers, W. Y. Kong, and D. Donadio, “UV–visible absorption spectra of solvated molecules by quantum chemical machine learning,” J Chem Theory Comput, vol. 18, no. 8, pp. 4891–4902, 2022.
36. Mortazavi, “Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials,” Adv Energy Mater, vol. 15, no. 9, p. 2403876, 2025.
37. Singh, T. Rugamba, H. Katara, and K. S. Grewal, “Computationally effective machine learning approach for modular thermal energy storage design,” Appl Energy, vol. 377, p. 124430, 2025.
38. S. K. Korkua, U. Thubsuang, S. Sakphrom, S. K. Dash, C. Tesanu, and K. Thinsurat, “Simulation-Driven Optimization of Thermochemical Energy Storage in SrCl2-Based System for Integration with Solar Energy Technology,” Inventions, vol. 10, no. 1, p. 9, 2025.
39. Martínez and P. Arévalo, “Distributed Peer-to-Peer Optimization Based on Robust Reinforcement Learning with Demand Response,” A Review. Computers, vol. 14, no. 2, p. 65, 2025.
40. Z. Dong, Y. Tao, S. Lai, T. Wang, and Z. Zhang, “Powering Future Advancements and Applications of Battery Energy Storage Systems Across Different Scales,” Energy Storage and Applications, vol. 2, no. 1, p. 1, 2025.