Hydrogen diffusion in TiCr$_2$H$_x$ Laves phases: A combined ab initio and machine-learning-potential study
Pranav Kumar, Fritz K\"ormann, Kaveh Edalati, Blazej Grabowski, Yuji Ikeda

TL;DR
This study combines ab initio calculations and machine learning to analyze hydrogen diffusion mechanisms and barriers in TiCr2H_x Laves phases, revealing concentration-dependent diffusion behavior and the influence of defects.
Contribution
It provides a comprehensive computational analysis of hydrogen diffusion pathways, barriers, and dynamics in TiCr2H_x alloys using DFT and MLIPs, highlighting the effects of concentration and defects.
Findings
Hydrogen diffusion barriers are higher for paths breaking Ti-H bonds.
Hydrogen prefers interstitial paths involving Cr-H bonds.
Diffusion coefficients show non-monotonic concentration dependence.
Abstract
The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCrH (0 < <= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent paths between the first-nearest-neighbor face-sharing interstitial sites. The hydrogen migration barriers are substantially higher for the paths that require breaking a Ti-H bond than for those that require breaking a Cr-H bond. Molecular dynamics (MD) simulations with the MLIPs also demonstrate that hydrogen migration occurs more frequently within the hexagonal rings made of the AB interstitial paths, each requiring the breaking of Cr-H bonds, than along the inter-ring paths. The diffusion coefficients of hydrogen obtained from…
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Taxonomy
TopicsHydrogen Storage and Materials · Hydrogen embrittlement and corrosion behaviors in metals · Machine Learning in Materials Science
