Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data

Abstract

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been shown to reversibly store hydrogen at room temperature, but the design of HEAs with appropriate thermodynamics is still challenging. To explore HEAs with appropriate hydride formation enthalpy, this study employs machine learning (ML), in particular, Gaussian process regression (GPR) using four different kernels by training with 420 datum points collected from literature and curated here. The developed ML models are used to predict the formation enthalpy of hydrides for the TixZr2-xCrMnFeNi (x = 0.5, 1.0 and 1.5) system, which is not in the training set. The predicted values by ML are consistent with data from experiments and density functional theory (DFT). The present study thus introduces ML as a rapid and reliable approach for the design of HEAs with hydride formation enthalpies of -25 to -39 kJ/mol for hydrogen storage at room temperature.

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• Hydrogen diffusion in TiCr${}_2$H${}_x$ Laves phases: A combined ab initio and machine-learning-potential study
• 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
• Magnesium-graphene interphase boundaries created by high-pressure torsion enhance hydrogen storage kinetics:Mechanisms and significance of activation energy and frequency factor
• Nanolamellar Hybrid High-Entropy Alloys with Superior Micromechanical Properties