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First-principles studies on vacancy-modified interstitial diffusion mechanism of oxygen in nickel, associated with large-scale atomic simulation techniques

Evidence and attribution

Authority of statements

Prose below summarizes the publication identified by doi, title, and pdf_path in the front matter. For definitive numerical values and figures, use the peer-reviewed article.

Summary

Oxygen diffusivity in fcc Ni is reassessed by combining first-principles thermodynamics with large-scale atomistic techniques. A simple octahedral–tetrahedral–octahedral interstitial path alone underestimates migration barriers and overpredicts diffusivities versus experiment; incorporating vacancy effects brings diffusivities in line with measurements. At high temperature, vacancy concentration is argued to be comparable to oxygen solubility, with strong O–vacancy binding and charge redistribution that couples oxygen transport to the vacancy field (abstract; introduction opening, extract). The J. Appl. Phys. article motivates a multiscale workflow where DFT-based free energies seed kinetic models that are cross-checked by ReaxFF/MEAM/kMC tools.

Methods

Oxygen diffusivity in dilute fcc Ni is formulated with an interstitial jump-rate expression consistent with Eyring transition-state theory, using finite-temperature migration free energies built from 0 K DFT energies plus vibrational (phonon DOS or Debye) and thermal electronic contributions to the Helmholtz free energy \(F(V,T)\). This first-principles thermodynamic route is combined with large-scale atomistic kinetics: ReaxFF molecular dynamics, MEAM molecular dynamics, and kinetic Monte Carlo as complementary tools to the DFT-based diffusion framework (abstract; computational methods section opening, extract pages 1–2).

Supercell sizes, diffusion jump networks, and convergence tests for Ni–O–vacancy interactions are specified in papers/Fang_J_App_Phys_NiO_diffusion_2014.pdf alongside tables linking computed diffusivities to experimental ranges cited in the paper.

1 — MD application (ReaxFF / MEAM MD)

  • Engine / code: ReaxFF MD and MEAM MD are used as large-scale atomistic complements to the DFT-based diffusion framework (abstract; computational overview).
  • System size & composition: Ni–O bulk and defect supercells containing thousands of atoms in the reported MD benchmarks (qualitative scale statement—confirm exact counts in pdf_path).
  • Boundaries / periodicity: bulk MD benchmarks use 3D PBC supercells as standard for Ni diffusion studies; confirm cell choices in the article.
  • Ensemble: NVT molecular dynamics is typical for these diffusion benchmarks unless the authors specify NPT segments—N/A in this wiki summary for explicit labels.
  • Timestep / thermostat / duration: N/A in this wiki summary—see pdf_path for fs timestep, thermostat, and ps/ns production lengths.
  • Barostat / pressure control: N/A — hydrostatic pressure control is not stated for the summarized MD portions; confirm whether any NPT segments appear in the PDF.
  • Pressure targets: N/A — not specified in this wiki summary for MD trajectories.

2 — Force-field training

N/A — not a ReaxFF parameterization paper; ReaxFF/MEAM are interatomic models used for kinetics comparisons as described in the article.

3 — Static QM / DFT and kinetic modeling

  • Functional / dispersion / basis / k-sampling: specified in the J. Appl. Phys. computational methods for 0 K DFT energies and finite-temperature corrections (phonon/Debye and thermal electronic contributions to \(F(V,T)\)) feeding Eyring-type jump rates.
  • Structures / pathways: O diffusion in dilute fcc Ni with interstitial paths and vacancy-coupled scenarios as developed in the paper.
  • Properties: migration free energies, diffusivities, and comparison to experiment (abstract; tables in PDF).
  • Kinetic Monte Carlo: kMC is included among the atomistic kinetics tools alongside MD (abstract).

Findings

1 — Outcomes and mechanisms

Considering only the octahedral–tetrahedral–octahedral interstitial path underestimates the migration barrier and overpredicts diffusivities by orders of magnitude versus experiment. Incorporating vacancies brings predicted diffusivities in line with measurements. First-principles analysis further argues that at high temperature the vacancy concentration can be comparable to oxygen solubility, with strong O–vacancy binding and charge redistribution, so oxygen transport is coupled to the vacancy field rather than behaving as a simple interstitial gas (abstract; extract pages 1–2).

2 — Comparisons

  • Computed diffusivities vs experimental ranges and vs interstitial-only models (abstract; tables in pdf_path).

3 — Sensitivity / design levers

  • Vacancy content and O–vacancy binding strongly modulate effective O transport versus simple interstitial pictures (abstract).

4 — Limitations / outlook

  • Experimental O diffusion data in Ni remain scattered; Eyring-type rate modeling carries approximations stated in the article (## Limitations).

5 — Corpus / KB honesty

The discussion frames this coupling as a practical correction to interstitial-only pictures used in some alloy oxidation models; quantitative barriers and kMC network details must be taken from pdf_path, not this page alone.

Limitations

Experimental oxygen diffusion data in Ni remain scattered; modeling focuses on selected pathways and approximations in Eyring-type rate formulations as presented in the article.

Wiki prose here is a navigation aid. Definitive numbers, protocol details, and figure-level claims should be taken from the peer-reviewed article at pdf_path (and any Supporting Information cited there), not from this page alone.

Citations and evidence anchors

  • DOI 10.1063/1.4861380 (extract).
  • Abstract and introduction (extract pages 1–2).