Machine Learning-Assisted Discovery and Optimization of Sodium-Ion Batteries: A Review
DOI:
https://doi.org/10.62411/jimat.v3i1.15954Keywords:
Sodium-ion batteries, Machine learning, Electrode materials, High-throughput screening, Energy storage systemsAbstract
Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries due to the natural abundance, low cost, and wide geographic availability of sodium resources. However, their practical implementation is hindered by challenges such as lower energy density, slower ion diffusion, and limited cycle stability. In recent years, machine learning (ML) has been increasingly applied to accelerate the discovery, design, and optimization of SIB materials and systems. This review provides a comprehensive overview of ML applications in sodium-ion battery research, including electrode material discovery, electrolyte optimization, performance prediction, and degradation analysis. Various ML techniques, such as supervised learning, unsupervised learning, and deep learning, are discussed in relation to their roles in materials informatics. Additionally, challenges such as data scarcity, model interpretability, and transferability are critically analyzed. Finally, future perspectives on integrating ML with high-throughput experiments and quantum computing are highlighted to guide next-generation sodium-ion battery research.References
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559, 547–555.
Ramprasad, R., Batra, R., Pilania, G., et al. (2017). Machine learning in materials informatics. npj Computational Materials, 3, 54.
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019). Recent advances of ML in materials science. npj Computational Materials, 5, 83.
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020). Big-data science in porous materials. Nature Reviews Materials, 5, 327–341.
Unocic, R. R., et al. (2021). Data-driven design of battery materials. Nature Reviews Materials, 6, 723–743.
Noé, F., Tkatchenko, A., Müller, K. R., & Clementi, C. (2020). Machine learning for molecular simulation. Annual Review of Physical Chemistry, 71, 361–390.
Xu, Y., et al. (2020). Recent advances in sodium-ion batteries. Advanced Energy Materials, 10, 2001239.
Wang, Y., et al. (2021). Sodium-ion batteries: materials and challenges. Advanced Energy Materials, 11, 2003155.
Wu, X., et al. (2022). Challenges and perspectives of SIBs. Nano Energy, 95, 106944.
Zhang, H., et al. (2023). Sodium-ion battery progress. Energy Storage Materials, 55, 123–145.
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai. Quantum machine learning for ABO3 perovskite structure prediction. Computational Materials Science, Volume 250, Pages 113694, 2025, https://doi.org/10.1016/j.commatsci.2025.113694.
S Rustad, M Akrom, T Sutojo, HK Dipojono. A feature restoration for machine learning on anti-corrosion materials. Case Studies in Chemical and Environmental Engineering 10, 100902, 2024, https://doi.org/10.1016/j.cscee.2024.100902.
Akrom, M., Rustad, S. & Dipojono, H.K. Investigation of Corrosion Inhibition Capability of Pyridazine Compounds via Ensemble Learning. J. of Materi Eng and Perform 34, 14948–14962 (2025). https://doi.org/10.1007/s11665-024-10129-x.
Muhamad Akrom, Wise Herowati, De Rosal Ignatius Moses Setiadi. A quantum circuit learning-based investigation: A case study in iris benchmark dataset binary classification. Journal of Computing Theories and Applications. Volume 2, Issue 3, Pages 355-367, 2024, https://doi.org/10.62411/jcta.11779.
Muhamad Akrom, Supriadi Rustad, Totok Sutojo, De Rosal Ignatius Moses Setiadi, Pulung Nurtantio Andono, Guruh Fajar Shidik, Hermawan Kresno Dipojono, Ryo Maezono. A novel quantum-enhanced model cascading approach based on support vector machine in blood-brain barrier permeability prediction. Materials Today Communications, Volume 45, Pages 112341, 2025, https://doi.org/10.1016/j.mtcomm.2025.112341.
Muhamad Akrom, Usman Sudibyo, Achmad Wahid Kurniawan, Noor Ageng Setiyanto, Ayu Pertiwi, Aprilyani Nur Safitri, Novianto Hidayat, Harun Al Azies, Wise Herawati. Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi. JoMMiT: Jurnal Multi Media dan IT, Volume 7, Issue 1, Pages 015-020, 2023, https://doi.org/10.46961/jommit.v7i1.721.
M. Akrom, T. Sutojo, A. Pertiwi, S. Rustad, H.K. Dipojono, Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds, J Phys Conf Ser, 2673(1), 012014 (2023), https://doi.org/10.1088/1742-6596/2673/1/012014.
M. Akrom, Green corrosion inhibitors for iron alloys: a comprehensive review of integrating data-driven forecasting, density functional theory simulations, and experimental investigation. J Mult Mater Inf, 1(1), 22–37 (2024), https://doi.org/10. 62411/jimat.v1i1.10495
M. Akrom, S. Rustad, H.K. Dipojono, A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors, Phys Scr, 99(3), 036006 (2024), https://doi.org/10.1088/1402-4896/ad28a9.
M. Akrom, S. Rustad, H.K. Dipojono, Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors, Results in Chemistry 6 (2023) 101126, https://doi.org/10.1016/j. rechem.2023.101126.
M. Akrom, S. Rustad, A.G. Saputro, H.K. Dipojono, Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors, Comput. Theor. Chem. 1229 (2023) 114307, https://doi.org/10.1016/ J.COMPTC.2023.114307.
M. Akrom, S. Rustad, H.K. Dipojono, Prediction of Anti-Corrosion performance of new triazole derivatives via Machine learning, Comput. Theor. Chem. 1236 (2024), https://doi.org/10.1016/j.comptc.2024.114599.
M. Akrom, Investigation of natural extracts as green corrosion inhibitors in steel using density functional theory, Jurnal Teori dan Aplikasi Fisika, 10(1), 89-102 (2022), https://doi.org/10.23960%2Fjtaf.v10i1.2927.
M. Akrom, S. Rustad, H.K. Dipojono. Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds. Materials Today Communications, 39, 108758 (2024), https://doi.org/10.1016/j.mtcomm.2024.108758.
M. Akrom, S. Rustad, H.K. Dipojono, SMILES-based machine learning enables the prediction of corrosion inhibition capacity, MRS Commun 14 (2024) 379–387, https://doi.org/10.1557/s43579-024-00551-6.
M. Akrom, S. Rustad, A.G. Saputro, A. Ramelan, F. Fathurrahman, H.K. Dipojono, A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds, Mater. Today Commun. 35 (2023) 106402, https://doi.org/10.1016/J. MTCOMM.2023.106402.
M. Akrom, et al., DFT and microkinetic investigation of oxygen reduction reaction on corrosion inhibition mechanism of iron surface by Syzygium Aromaticum extract, Appl. Surf. Sci. 615 (2023), https://doi.org/10.1016/j. apsusc.2022.156319.
M. Akrom, S. Rustad, H.K. Dipojono. Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds. Materials Today Quantum, 2, 100007 (2024), https://doi.org/10.1016/j.mtquan.2024.100007.
Muhamad Akrom, Supriadi Rustad, Hermawan Kresno Dipojono, Ryo Maezono, Hideaki Kasai. Enhanced quantum support vector regression with quantum kernels and virtual sampling for ABX3 perovskite formation energy. Expert Systems with Applications, Pages 130817, 2025, https://doi.org/10.1016/j.eswa.2025.130817.
Muhamad Akrom. Quantum Neural Network in Architectures, Learning Mechanisms, and Emerging Applications Across Domains: A Review. Journal of Multiscale Materials Informatics, Volume 2, Issue 2, Pages 30-39, 2025, 10.62411/jimat.v2i2.14929.
Downloads
Published
Issue
Section
License
Authors who publish their articles in this journal agree to the following conditions:
- Copyright remains with the author and gives the JIMAT journal the right as first priority to publish the article under a Creative Commons Attribution License which allows articles to be shared with acknowledgment of the author of the article and this journal as the place of publication.
- Authors can distribute their published articles non-exclusively (for example: in university repositories or in books) with notification or acknowledgment of publication in JIMAT.
- Authors are permitted to list their work online (for example: on a personal website or in a university repository) before and after the submission process (see The Effect of Open Access).
