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All issues Volume 417 (2025) MATEC Web Conf., 417 (2025) 06015 References
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MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
Article Number 06015
Number of page(s) 11
Section Computational & Data-driven Modelling
DOI https://doi.org/10.1051/matecconf/202541706015
Published online 25 November 2025
  1. J.A. Turner, Sustainable hydrogen production. Sci., 305 972 (2004). DOI:10.1126/science.1103197 [Google Scholar]
  2. I. Staffell, D. Scamman, A. V. Abad, P Balcombe, P. Dodds, P Ekins, N Shah and K. R Ward, The role of hydrogen and fuel cells in the global energy system. Energy. Environ. Sci., 12 463 (2019). https://doi.org/10.1039/C8EE01157E [Google Scholar]
  3. L. Schlapbach and A. Züttel Hydrogen-storage materials for mobile applications. Nature, 414 353 (2001) DOI:10.1142/9789814317665_0038 [CrossRef] [PubMed] [Google Scholar]
  4. B. Sakintuna, F. Lamari-Darkrim, and M Hirscher, Metal hydride materials for solid hydrogen storage, A review. Int. J. Hydrog. Energy, 9 32 (2007) DOI:10.1016/j.ijhydene.2006.11.022 [Google Scholar]
  5. M. Lototskyy, V. A Yartys, B. G Pollet and R. C Bowman, Metal hydride hydrogen compressors, A review. Int. J. Hydrog. Energy, 11 39 {(2004) DOI.org:10.1016/j.ijhydene.2014.01.158 [Google Scholar]
  6. H. Wang, J. Zhang and E. Akiba, Hydrogen storage properties of Ti–V–Cr alloys with body-centered cubic structure. Journal of Alloys and Compounds, 2 377 (2004) DOI.org/10.1016/j.ijhydene.2023.07.020 [Google Scholar]
  7. K. Tanaka, K. Sato and T. Ichikawa, Improvement of hydrogen storage characteristics of Ti–V–Cr alloys by alloying. J. Alloys and Compd, 404 406 (2005) DOI.org/10.1016/j.matchemphys.2024.129132 [Google Scholar]
  8. X. Luo, and E. Akiba, Effects of Al substitution on Ti–V–Cr BCC solid solution hydrogen storage alloys. J. Alloys and Compd, 1 311 (2000) DOI:10.1016/j.matchemphys.2024.129132 [Google Scholar]
  9. X. Guo, H. Lin, and G. Liang, Synthesis and hydrogen storage properties of Ti–V–Cr– Fe–Mn BCC alloys. J. Alloys and Compd, 2 505 (2010) DOI:10.1016/j.ijhydene.2021.06.137 [Google Scholar]
  10. K. T Butler, D. W Davies, H. Cartwright, O. Isayev, and A. Walsh, Machine learning for molecular and materials science. Nature, 559 7715 (2018) DOI: 10.1038/s41586-018-0337-2 [Google Scholar]
  11. T. Lookman, P. V. Balachandran, D. Xue and R. Yuan, Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. Npj Comput. Mater., 1 5 (2019) DOI: 10.1038/s41524-019-0153-8 [Google Scholar]
  12. C. Kim, G. Pilania and R. Ramprasad, Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX3 perovskite. J. Phys. Chem. A, 27 120 (2016) DOI: 10.1021/acs.jpcc.6b05068. [Google Scholar]
  13. M Fernandez, V Zelenay and A Balducci, Machine learning predictions of hydride formation enthalpy for energy storag. Mater. Today Energy, 20 10069 (2021) DOI: 10.1016/j.ensm.2023.102964 [Google Scholar]
  14. R. Jalem, M. Nakayama, M. Wakihara, and T. Kasuga, Machine learning accelerated screening of hydride materials for hydrogen storage. Comput. Mater. Sci, 183 109795 (2020) DOI: 10.1039/C3TA13235H [Google Scholar]
  15. L. Ma, Y. Zuo, T. Xie and S. P. Ong, Machine learning for electrochemical energy materials. Energy Environ Sci, 2 13 (2020) DOI: 10.1016/j.mtsust.2025.101163 [Google Scholar]
  16. Y. Zhang, T. Xie, Y. Wang, and J. Sun, Machine-learning predictions of hydrogen storage capacities of high-entropy alloys. Mater. Sci. Technol, 82 86 (2021) DOI:10.1016/j.pmatsci.2022.101018 [Google Scholar]
  17. L. Xie, Y. Zhu, W. Wang and H. Chen, Deep learning for predicting phase formation in high-entropy alloys. Acta Mater, 224 117505 (2022) Doi.10.1016/j.jmrt.2022.01.172 [Google Scholar]
  18. W. Xu, Y. Wang and X. Fang, Artificial neural network prediction of plateau pressures in metal hydrides. Int. J. Hydrog. Energy, 33 46 (2021) DOI:10.1016/j.jmrt.2022.01.172 [Google Scholar]
  19. B. Vyas, A. Khatiashvili, L. Galati, K. Ngo, N. Gildeener-Leapman, M. Larsen and I. K. Lednev, Raman hyperspectroscopy of saliva and machine learning for Sjögren’s disease diagnostics. Sci Rep, 14 11135 (2024) DOI: 10.1038/s41598-024-59850-6 [Google Scholar]
  20. R.Y. Coley, Q. Liao and N. Simon,., Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: a case study in suicide risk prediction, BMC Med Res Methodol, 23 33 (2023) DOI: 10.1186/s12874-023-01844-5 [Google Scholar]
  21. K. Mnich, A. Polewko-Klim, A.K. Golińska, W. Lesiński and W.R. Rudnicki, Super learning with repeated cross validation. ICDMW IEEE 629 635 (2020) DOI:10.48550/arXiv.2003.08342 [Google Scholar]

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