Machine learning driven search of hydrogen storage materials
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arXiv
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2025
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| author | Banerjee, Tanumoy Ji, Kevin Xia, Weiyi Ouyang, Gaoyuan Del Rose, Tyler Hlova, Ihor Z. Ueland, Benjamin Johnson, Duane D. Wang, Cai-Zhuan Balasubramanian, Ganesh Singh, Prashant |
| author_facet | Banerjee, Tanumoy Ji, Kevin Xia, Weiyi Ouyang, Gaoyuan Del Rose, Tyler Hlova, Ihor Z. Ueland, Benjamin Johnson, Duane D. Wang, Cai-Zhuan Balasubramanian, Ganesh Singh, Prashant |
| contents | The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning driven search of hydrogen storage materials Banerjee, Tanumoy Ji, Kevin Xia, Weiyi Ouyang, Gaoyuan Del Rose, Tyler Hlova, Ihor Z. Ueland, Benjamin Johnson, Duane D. Wang, Cai-Zhuan Balasubramanian, Ganesh Singh, Prashant Materials Science Machine Learning The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage. |
| title | Machine learning driven search of hydrogen storage materials |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2503.04027 |