Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

Banerjee, Tanumoy; Ji, Kevin; Xia, Weyi; Ouyang, Gaoyuan; Rose, Tyler Del; Hlova, Ihor Z.; Ueland, Benjamin; Johnson, Duane D.; Wang, Cai-Zhuang; Balasubramanian, Ganesh; Singh, Prashant

doi:10.1016/j.ijhydene.2025.06.106

Title: Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

Journal Article · Wed Jul 09 00:00:00 UTC 2025 · International Journal of Hydrogen Energy

DOI: https://doi.org/10.1016/j.ijhydene.2025.06.106 · OSTI ID:2573065

^[1]; Ji, Kevin ^[2]; Xia, Weyi ^[2]; Ouyang, Gaoyuan ^[2]; Rose, Tyler Del ^[2]; Hlova, Ihor Z. ^[2];

^[2]; Johnson, Duane D. ^[3]; Wang, Cai-Zhuang ^[2]; Balasubramanian, Ganesh ^[4];

^[2]

Ames Laboratory (AMES), Ames, IA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Ames Laboratory (AMES), Ames, IA (United States)
Ames Lab., and Iowa State Univ., Ames, IA (United States)
University of New Haven, West Haven, CT (United States)

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 medium-entripy 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 results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti₅Mo₉₅ alloys. Density functional theory (DFT) and molecular dynamics (MD) 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. Here, 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.

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Research Organization:: Ames Laboratory (AMES), Ames, IA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: AC02-05CH11231; AC02-07CH11358

OSTI ID:: 2573065

Journal Information:: International Journal of Hydrogen Energy, Journal Name: International Journal of Hydrogen Energy Vol. 154; ISSN 0360-3199

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

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