@article{bibcite_36889,
  author = {Tanumoy Banerjee and Kevin Ji and Weyi Xia and Gaoyuan Ouyang and Tyler Del Rose and Ihor Z Hlova and Benjamin Ueland and Duane D Johnson and Cai-Zhuang Wang and Ganesh Balasubramanian and Prashant Singh},
  title = {Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications},
  abstract = {<p>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 <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/metal-hydride">metal hydrides</a> 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 <a href="https://www.sciencedirect.com/topics/engineering/thermodynamic-parameter">thermodynamic parameters</a> and local <a href="https://www.sciencedirect.com/topics/engineering/lattice-distortion">lattice distortion</a> (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{\textendash}Nb-X (X\&nbsp;=\&nbsp;Mo, Cr, Hf, Ta, V, Zr) and Co{\textendash}Ni-X (X\&nbsp;=\&nbsp;Al, Mg, V). Ti{\textendash}Nb{\textendash}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{\textendash}50\&nbsp;\%. 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 Ti<sub>5</sub>Mo<sub>95</sub> alloys. <a href="https://www.sciencedirect.com/topics/physics-and-astronomy/density-functional-theory">Density functional theory</a> (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. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H<sub>2</sub> wt\% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance <a href="https://www.sciencedirect.com/topics/engineering/hydrogen-solubility">hydrogen solubility</a> and storage.</p>},
  year = {2025},
  booktitle = {International Journal of Hydrogen Energy},
  journal = {International Journal of Hydrogen Energy},
  series = {International Journal of Hydrogen Energy},
  volume = {154},
  pages = {149916},
  month = {08/2025},
  institution = {Elsevier BV},
  publisher = {Elsevier BV},
  issn = {0360-3199},
  url = {https://doi.org/10.1016/j.ijhydene.2025.06.106},
  doi = {10.1016/j.ijhydene.2025.06.106},
}