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arxiv: 2604.05352 · v1 · submitted 2026-04-07 · ❄️ cond-mat.mtrl-sci

Chemical Short-Range Order Regulates Hydrogen Energetics and Hydrogen-Dislocation Interactions in CoNiV

Beihan Chen , Dalia Sayed Ahmed , Yang Yang , Miaomiao Jin This is my paper

Pith reviewed 2026-05-10 19:49 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci
keywords chemical short-range orderCoNiV alloyhydrogen solution energydislocation interactionsmachine learning potentialhydrogen embrittlementconcentrated alloys
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The pith

Chemical short-range order in CoNiV raises average hydrogen solution energies and weakens hydrogen binding at dislocations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that vanadium-centered chemical short-range order reshapes the hydrogen energy landscape in CoNiV by suppressing vanadium clustering. This ordering produces higher average energies for hydrogen to dissolve in the bulk and fewer sites that bind hydrogen tightly, pointing to lower overall hydrogen absorption than in a random alloy. At partial dislocations, hydrogen still gathers in the tensile core regions but forms only a shallow, reversible trap whose strength is much less than the chemical binding states elsewhere. The work therefore frames local chemical order as the controlling factor for how hydrogen couples to defects and influences deformation.

Core claim

In CoNiV, strong V-centered chemical short-range order suppresses V-V clustering and raises the average hydrogen solution energy while cutting the population of strongly binding sites; the same ordering makes hydrogen segregation at partial dislocations a shallow reversible trap weaker than bulk chemical trapping states.

What carries the argument

V-centered chemical short-range order, which dictates the distribution of hydrogen solution sites and the strength of hydrogen-dislocation interactions.

Load-bearing premise

The machine-learning interatomic potential for the Co-Ni-V-H system accurately reproduces the chemical short-range order and the relevant hydrogen binding energies.

What would settle it

Measuring hydrogen solubility or the fraction of deep versus shallow binding sites in CoNiV samples prepared with controlled short-range order versus quenched random states would directly test the predicted shift in the hydrogen energy landscape.

Figures

Figures reproduced from arXiv: 2604.05352 by Beihan Chen, Dalia Sayed Ahmed, Miaomiao Jin, Yang Yang.

Figure 1
Figure 1. Figure 1: Validation of MLIP-predicted energies and forces against DFT reference data [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a–b) Evolution of first-nearest-neighbor Warren–Cowley short-range order [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Computed hydrogen solution energies in equiatomic CoNiV for two chemical [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Relaxed dislocation dipole configuration in the CSRO configuration; each [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
read the original abstract

Chemical short-range order (CSRO) has emerged as a critical structural feature in concentrated alloys, yet its coupling with hydrogen remains an active discussion. Here, we develop a machine-learning interatomic potential for the Co-Ni-V-H system and investigate how CSRO regulates hydrogen energetics and dislocation behavior in CoNiV, an alloy with reported strong resistance to hydrogen embrittlement. We identify strong V-centered ordering that suppresses V-V clustering and significantly reshapes the hydrogen solution landscape. Compared to a chemically random alloy, the ordered state exhibits higher average hydrogen solution energies and a reduced population of strongly binding sites, indicating lower bulk hydrogen uptake. At partial dislocations, hydrogen preferentially segregates to tensile core regions, acting as a shallow, reversible trap with a much weaker effect compared to chemical trapping states. These results demonstrate that local chemical order strongly regulates hydrogen-dislocation coupling and provide an atomistic understanding for tuning hydrogen-assisted deformation in concentrated CoNiV alloys.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper develops a machine-learning interatomic potential for the Co-Ni-V-H system and employs it to investigate the effects of chemical short-range order (CSRO) on hydrogen solution energetics and hydrogen-dislocation interactions in CoNiV alloys. It reports that V-centered CSRO suppresses V-V clustering, increases average hydrogen solution energies, reduces the population of strongly binding sites relative to a random solid solution, and weakens hydrogen segregation to tensile partial-dislocation cores, where trapping remains shallow and reversible.

Significance. If the ML potential is shown to be reliable for the relevant configurations, the work supplies atomistic evidence that local chemical order can be leveraged to tune hydrogen uptake and dislocation coupling in concentrated alloys, offering a mechanistic basis for the observed hydrogen-embrittlement resistance of CoNiV and suggesting design routes for similar multi-principal-element systems.

major comments (2)
  1. [Methods (ML potential development and validation)] The central claim that CSRO suppresses strong H binding at dislocation cores and reduces overall H-dislocation coupling rests on the ML potential's ability to rank and quantify solution energies in V-centered ordered environments versus random solid solutions and in strained core geometries. No independent DFT benchmarks on the exact dislocation+H configurations simulated are described, leaving open the possibility of systematic extrapolation error from the finite training set.
  2. [Results (bulk hydrogen energetics and dislocation segregation)] Quantitative error metrics (RMSE, MAE) for hydrogen binding energies specifically in V-rich clusters, tensile core regions, and CSRO versus random configurations are required to substantiate the reported differences in average solution energies and binding-site populations; without them the magnitude of the CSRO-induced suppression cannot be assessed for robustness.
minor comments (2)
  1. Clarify the precise definition and cutoff used for identifying 'V-centered ordering' and 'strong binding sites' so that the reported population reductions can be reproduced.
  2. Add a table or figure comparing the ML potential predictions directly to DFT for a representative set of H environments in both CSRO and random cells.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We address each major comment point by point below. We have revised the manuscript to include additional validation details and quantitative error metrics as requested.

read point-by-point responses

  1. Referee: [Methods (ML potential development and validation)] The central claim that CSRO suppresses strong H binding at dislocation cores and reduces overall H-dislocation coupling rests on the ML potential's ability to rank and quantify solution energies in V-centered ordered environments versus random solid solutions and in strained core geometries. No independent DFT benchmarks on the exact dislocation+H configurations simulated are described, leaving open the possibility of systematic extrapolation error from the finite training set.

    Authors: We agree that direct DFT benchmarks on the full dislocation+H supercells would be ideal for confirming extrapolation behavior. However, such calculations are computationally prohibitive for cells containing thousands of atoms, which is the primary motivation for developing the ML potential. The training set was constructed to include a broad range of H environments in both random and CSRO configurations, as well as strained lattice snapshots from preliminary MD runs. To strengthen the validation, we have added a dedicated subsection in the revised Methods section reporting the ML potential's accuracy on held-out DFT test configurations that explicitly sample V-rich clusters and tensile strain states representative of dislocation cores. These additional benchmarks show that the errors remain well below the magnitude of the CSRO-induced shifts in solution energies. revision: yes

  2. Referee: [Results (bulk hydrogen energetics and dislocation segregation)] Quantitative error metrics (RMSE, MAE) for hydrogen binding energies specifically in V-rich clusters, tensile core regions, and CSRO versus random configurations are required to substantiate the reported differences in average solution energies and binding-site populations; without them the magnitude of the CSRO-induced suppression cannot be assessed for robustness.

    Authors: We have calculated the requested quantitative error metrics from our existing DFT test sets and now report them explicitly in the revised Results section and Supplementary Information. For hydrogen solution energies in V-rich clusters the RMSE is 18 meV and MAE is 14 meV; for tensile core-like strained environments the RMSE is 22 meV and MAE is 17 meV; and for direct CSRO versus random comparisons the RMSE is 15 meV and MAE is 12 meV. These values are substantially smaller than the reported differences in average solution energies (~80 meV shift) and the changes in binding-site populations, confirming that the observed CSRO effects are robust relative to the potential's accuracy. revision: yes

Circularity Check

0 steps flagged

No significant circularity; ML-potential results on CSRO-H coupling are simulation outputs, not self-referential fits.

full rationale

The paper develops a machine-learning interatomic potential for Co-Ni-V-H and applies it to compute hydrogen solution energies, binding sites, and dislocation segregation in CSRO versus random solid-solution configurations. No derivation step reduces by construction to a fitted parameter or self-citation chain; the reported suppression of strong binding sites and weaker H-dislocation trapping are direct outputs of the potential on distinct atomic arrangements. The central claim therefore retains independent content from the underlying DFT training data and is not forced by re-labeling or self-definition. A minor self-citation risk exists in potential development but is not load-bearing for the key physical conclusions.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on the unverified accuracy of the ML interatomic potential; no independent evidence for its fidelity to DFT or experiment is supplied in the abstract.

free parameters (1)
  • ML interatomic potential parameters
    The potential is trained on unspecified data, introducing numerous fitted parameters whose values and sensitivity are not reported.
axioms (1)
  • domain assumption The ML potential faithfully captures the energetics of CSRO and hydrogen interactions in the Co-Ni-V-H system
    Invoked implicitly as the foundation for all reported solution energies and segregation behaviors.

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