Interpretable machine learning framework for hydrogen storage capacity prediction in Ti-Zr-Mn-Cr-V high-entropy alloys: Influenced by valence electron average

Journal of Energy Storage 2026

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Authors: Xuewu Liu, W. Li, Licheng Liang, Chunqi Zhang, Bo Zheng, Lan Feng, Quanbing Ren, Qingqing Yang, Fei Liang

DOI: 10.1016/j.est.2026.121649

Cited by: 2

References: 51

Fields of Study & Subjects

Machine Learning in Materials Science Hydrogen Storage and Materials Electrocatalysts for Energy Conversion

References 51