Machine Learning Potentials for Hydrogen Absorption in TiCr${}_2$ Laves Phases

Abstract

The energetics of hydrogen absorption in C15 cubic and C14 hexagonal TiCr${}_2$H${}_x$ Laves phases is investigated for $0<x\le 6$ with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). The MLIPs are trained with configurations generated through a series of active-learning schemes. Basin-hopping Monte Carlo (BHMC) simulations based on the MLIPs predict minimum-energy hydrogen configurations, along with enthalpies of formation and hydrogen orderings. The obtained phase transformations at 0 K agree well with the experiments at low temperatures. The hydrogen solubility limits in the low-concentration $\alpha$ phases at 0 K are predicted to be $x=1.0$ and $x=1.5$ for the C15 and the C14 phases, respectively. At these concentrations, C15 TiCr${}_2$H shows the $Cc$ monoclinic symmetry, while C14 TiCr${}_2$H${}_{1.5}$ shows the $Ama2$ orthorhombic symmetry, both of which have not been reported for this system. The first and the second hydride phases, i.e., $\beta$ and ${\beta }^{\prime }$, at 0 K are found around $x=3$ and $x=4$, respectively, for both the C15 and the C14 phases. In the second-hydride ${\beta }^{\prime }$ phases, C15 TiCr${}_2$H${}_4$ shows the $I{4}_1/a$ tetragonal symmetry, while C14 TiCr${}_2$H${}_4$ shows the $R\bar{3}c$ rhombohedral symmetry. Hydrogen repulsion are found to extend to edge-sharing interstices, affecting the hydrogen ordering. Furthermore, the $6h_2$ A${}_2$B${}_2$ interstices are found to be energetically substantially more preferable for C14 TiCr${}_2$H${}_x$ than the other A${}_2$B${}_2$ interstices at low hydrogen concentrations, influencing the hydrogen-occupation trend.

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Cited by in corpus (3)

• Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions
• 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