Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature

Shivam Dangwal; Pranav Kumar; Yuji Ikeda; Blazej Grabowski; Kaveh Edalati

doi:10.1016/j.ijhydene.2026.155840

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arXiv · 2606.04803

Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature

Shivam Dangwal·Pranav Kumar·Yuji Ikeda·Blazej Grabowski·Kaveh Edalati

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Abstract

High-entropy alloys (HEAs) have received considerable attention for hydrogen storage because of their compositional flexibility; however, designing HEAs with optimal thermodynamics is critical. This study employs machine learning via artificial neural networks (ANN) and density functional theory (DFT) to design a novel AB-type TixNb2-xVCrMnFe (x = 0.5-2.0) high-entropy system for hydrogen storage at ambient temperature (A: Ti, V and Nb, and B: Cr, Mn and Fe). Both ANN and DFT predict that the hydride formation enthalpy decreases to negative values with increasing the titanium content. Two alloys with x > 1.5 are predicted to achieve enthalpies within the -25 to -39 kJ/mol range, making them appropriate for room-temperature hydrogen storage. Experiments demonstrate good agreement with the enthalpy predictions, with the Ti-rich alloys showing reversible hydrogen storage with fast kinetics at room temperature. These results provide a framework for reliable use of data analysis and ab initio calculations to explore high-entropy hydrides as hydrogen storage materials.

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Shivam Dangwal, Pranav Kumar, Yuji Ikeda, Blazej Grabowski, Kaveh Edalati. 2026-06-03. Machine learning via artificial neural networks coupled with density functional theory and experiments for thermodynamic optimization of high-entropy alloys for hydrogen storage at room temperature. https://doi.org/10.1016/j.ijhydene.2026.155840

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