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REVIEW 3 major objections 6 minor 99 references

Nonadiabatic reactive scattering of hydrogen on different surface facets of copper

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Electron-hole friction barely affects H$_2$ dissociation on copper surfaces.

desk verdict A solid, reproducible computational study whose core 'friction is weak' conclusion is probably right, but the ODF surrogate error near the transition state needs a systematic Λ·v bound before the LDFA/ODF indistinguishability claim is fully trusted. read the letter →

arxiv 2505.18147 v1 pith:WMFIIAZP submitted 2025-05-23 physics.chem-ph cond-mat.mtrl-sci

Wojciech G. Stark , Connor L. Box , Matthias Sachs , Nils Hertl , Reinhard J. Maurer This is my paper · ORCID
classification physics.chem-phcond-mat.mtrl-sci
keywords dissociativechemisorptionelectronicfrictionelectron-holepairexcitationsmachinelearninginteratomicpotentialsatomicclusterexpansionstate-to-statescatteringhydrogenoncopperorbital-dependent
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to test whether electron-hole pair excitations, the main nonadiabatic energy-loss channel in metal-surface scattering, alter the outcome of H$_2$ dissociative chemisorption on copper. It builds full-dimensional machine-learned surrogate models of the electronic friction tensor for four copper facets (Cu(111), Cu(100), Cu(110), and Cu(211)), couples them to an existing machine-learned potential energy surface, and runs thousands of state-resolved scattering trajectories with and without friction. The central claim is that electron-hole pair influence is weak: sticking and survival probabilities are controlled by the shape of the potential energy surface and the initial vibrational state, while nonadiabatic effects only produce subtle changes in rovibrationally inelastic transition probabilities. If correct, this means the least expensive adiabatic simulations capture the essentials of hydrogen dissociation on copper, and the difference between simple isotropic friction models and full anisotropic ab initio friction models is not dynamically decisive. A sympathetic reader would care because these are the first transferable, publicly available models of their kind for hydrogen on copper.

What carries the argument

The central machinery is molecular dynamics with electronic friction (MDEF), a Langevin equation in which a friction tensor $\Lambda$ and a matching stochastic force dissipate nuclear energy into electron-hole pairs. Two friction models are represented: the isotropic local density friction approximation (LDFA), where the friction coefficient is a function of the local electron density, and orbital-dependent friction (ODF), where $\Lambda$ is built from Kohn–Sham electron–phonon coupling matrix elements at the Fermi level. The transferable full-dimensional surrogates are constructed with the atomic cluster expansion (ACE), a body-ordered basis that yields symmetric positive semi-definite friction tensors with the correct equivariance; the ODF surrogate uses a row-wise coupling ansatz that factors the friction tensor into a sum of outer products of 3×3 matrix-valued environment functions. These machine-learned friction models are combined with a machine-learned interatomic potential for the potential energy surface, enabling 20,000-trajectory quantum-state-resolved scattering simulations per state and energy on all four facets.

What would settle it

Measure the H$_2(v=2,J=1)\rightarrow(v=1,J=1)$ vibrational de-excitation probability from Cu(110) with state-resolved resolution better than a few tenths of a percent; the paper predicts a weak kinetic-energy dependence and only a fraction-of-a-percent nonadiabatic enhancement, so a several-fold enhancement at low collision energies would falsify the near-adiabaticity claim. A complementary check is the vibrational lifetime of a hydrogen atom chemisorbed on Cu(111) or Cu(110), where LDFA and ODF differ by more than a factor of three.

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Extended reading notes

Core claim

Using molecular dynamics with electronic friction (MDEF), the authors show that for quantum-state-resolved H$_2$ scattering on Cu(111), Cu(100), Cu(110), and Cu(211), the probability of dissociative adsorption is dominated by the topology of the potential energy surface and the initial vibrational quantum state, not by electron-hole pair excitation. The nonadiabatic contribution, modelled either with isotropic local-density friction (LDFA) or with full orbital-dependent friction (ODF) computed from first-order time-dependent perturbation theory, changes sticking and survival probabilities by only small amounts across all facets and collision energies. The one observable previously proposed as a sensitive fingerprint of nonadiabaticity, vibrational de-excitation H$_2(v=2,J=1)\rightarrow(v=1,J=1)$ on Cu(111), shows only a weak kinetic-energy dependence, and the difference between LDFA and ODF is substantially smaller than earlier low-temperature friction calculations suggested. Because the new friction models reproduce ab initio reference data along scattering and dissociation trajectories, the authors conclude that the computed near-adiabaticity is not an artefact of a crude friction estimate, and they present the combined potential and friction models as the most accurate publicly available full-dimensional models for H$_2$ on copper to date.

Load-bearing premise

The machine-learned potential energy surface was iteratively retrained until its reaction probabilities matched reference values, so the agreement with experiment and the conclusion that potential-energy shape controls dissociation partly inherit the fitness target of the fitting procedure rather than being purely independent predictions.

Editorial extensions

If this is right

  • For thermal sticking and survival probabilities of H$_2$ on the four low-index copper facets, adiabatic Born–Oppenheimer dynamics is sufficient; the added cost and complexity of electronic friction changes the result only slightly.
  • The facet identity and the initial vibrational state, not electron-hole pair friction, set the reactivity ordering: Cu(110) and Cu(211) react at lower translational energies when the molecule starts in $v=2$, while Cu(111) and Cu(100) stay inert until higher energies.
  • The previously reported kinetic-energy fingerprint of anisotropic nonadiabatic effects in H$_2$ vibrational de-excitation on Cu(111) does not survive when the full orbital-dependent friction expression is used instead of the low-temperature approximation, suggesting that the earlier anisotropy signal was likely an artefact of that approximation.
  • The publicly released machine-learned potential and friction models for all four facets allow future simulations that include surface phonons and electronic dissipation without re-fitting electronic structure data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If nonadiabaticity is this weak across all four facets, copper is a poor benchmark for testing electronic-friction theories; surfaces such as silver, gold, or platinum, where electron-hole pair coupling is stronger or barriers are shaped differently, would make sharper discriminators, and the paper's machinery transfers to them directly.
  • The near-degeneracy of LDFA and ODF in these dynamics suggests the cheaper isotropic friction model is sufficient for H$_2$ on copper, but the full tensorial model may still matter for observables that weight the chemisorbed state heavily, such as hot-electron yields or kinetic isotope effects.
  • The ODF surrogate deviates most from reference data near the transition state, where velocities are low, so the dynamical insensitivity may hide genuine friction-tensor errors; testing on heavier adsorbates with higher arrival velocities at the barrier would reveal whether the agreement is structural or coincidental.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper presents full-dimensional machine-learning surrogate models of the electronic friction tensor for H2 on Cu(111), Cu(100), Cu(110), and Cu(211): an isotropic LDFA model built from an ACE density surrogate, and an anisotropic orbital-dependent friction (ODF) model built with the ACE-friction framework of Sachs et al. Combined with a MACE interatomic potential for the H2/Cu PES taken from prior work by the same group, the models are used in molecular dynamics with electronic friction (MDEF) to compute state-resolved sticking, survival, and rovibrational transition probabilities with 20,000 trajectories per condition. The paper reports good agreement with absolute survival probabilities on Cu(100) and with the available Cu(110) data, finds that friction changes sticking and survival only slightly across all four facets, and concludes that electron-hole-pair effects are weak, that the PES shape and initial vibrational state control dissociation, and that LDFA and ODF friction give nearly indistinguishable results. It also reinterprets the previously reported Spiering-Meyer fingerprint of anisotropic friction on Cu(111) as an artefact of the low-temperature Lambda-zero approximation.

Significance. If the central conclusions hold, this is a valuable contribution: it delivers transferable, publicly available PES and EFT models for reactive hydrogen chemistry on multiple copper facets; it provides a systematic, statistically well-converged comparison of adiabatic, LDFA, and ODF dynamics against experimental state-resolved observables; and it makes falsifiable predictions (negligible effect of friction on sticking and survival; weak kinetic-energy dependence of nu=2 to nu=1 de-excitation; no experimentally resolvable nonadiabatic fingerprint on any of the four facets). The manuscript reports 20,000 trajectories per condition with bootstrap error estimates, documents DFT-level validation of the friction surrogates along representative trajectories, includes convergence studies for k-grid, broadening, and slab size, and makes code, data, and models publicly available; these are genuine strengths. The qualitative conclusion of weak nonadiabaticity is consistent with prior LDFA-based studies and is supported by both the LDFA and ODF simulations, so it is probably robust.

major comments (3)
  1. [Sec. III A / Figs. 3-4, S16-S17] The manuscript documents systematic deviations of the ACE-friction surrogate near the dissociation barrier, for example Lambda_dd on a Cu(211) dissociation trajectory and Lambda_phiphi on Cu(110) scattering (Fig. 3 and Fig. S11), and states that these persist in models retrained with different settings, indicating a systematic rather than statistical error. The dismissal of their dynamical impact argues that transition-state velocities are low, citing single example scattering trajectories per facet (Figs. S16-S17). This defense is not systematic and is weakest for the component that matters most for the paper's key nonadiabatic fingerprint (Fig. 9): via Eq. (1), Lambda_dd couples to the internal stretch, whose velocity for H2(nu=2) remains large (~0.1 Angstrom/fs) at the barrier, whereas the 'low velocity' argument strictly applies to the center-of-mass Z-mode near its turning point. Moreover, the deviations are documented on a dissociation trajectory, while the velocity illustration is taken from scattering trajectories, so the two do not directly confront each other. Because the BO/LDFA/ODF differences in Fig. 9 are at the 10^-3 level in probability and the documented Lambda_dd errors near the barrier are commensurate with the friction values themselves, I request a systematic quantification, e.g., the distribution of the error in the friction force Lambda-v along ensembles of dissociating and scattering trajectories, or a sensitivity test that injects the documented surrogate errors, before the 'even more subtle' LDFA-vs-ODF conclusion is drawn. I note that the main qualitative conclusion (friction is weak) is independently supported by the well-validated LDFA model and is not overturned by this concern.
  2. [Sec. II C a] The text reports that the MACE PES database was built by adaptive sampling with retraining 'until the final dynamical observable matched the reference values,' but it does not state what those reference values were. If, as in Ref. [58], they are dynamics results computed with DFT using the SRP48 functional, then the PES inherits the semi-empirical character of SRP48, which is parameterized for this very system, and the subsequent agreement with experiment is partly inherited rather than an independent test of the PES. The manuscript should state the nature of the reference values explicitly and discuss the independence of the experimental validation; this would also sharpen the central claim that dissociative adsorption is 'dominated by the shape of the underlying potential energy surface,' which otherwise risks being partially circular. This is a provenance and clarity issue: the authors are transparent about the refinement, but the key term 'reference values' is left undefined in a way that bears on the interpretation of the headline conclusion.
  3. [Abstract / Sec. III C] The abstract claims that 'the predicted sticking coefficient and survival probabilities are in excellent agreement with experiment,' but no direct quantitative comparison of sticking probabilities is presented. The Anger et al. sticking data discussed in Sec. III C are rovibrationally unassigned, are compared only in terms of facet ordering, and the paper itself notes that the experimental order differs from the computed nu=2,J=1 sticking. The quantitative experimental validation in Sec. III B concerns survival and rotational-excitation probabilities on Cu(100) (absolute, 500 K) and Cu(110) (one absolute point with +/-0.13 error plus slope-matched relative data). The abstract should either be revised to specify which observables are validated quantitatively, or a direct quantitative sticking comparison should be added.
minor comments (6)
  1. [Sec. II A] The sentence 'Lambda_ij(R,z) is a component of of the 3N x 3N EFT Lambda' contains a duplicated 'of.'
  2. [Sec. II C b] The sentence 'We evaluate the the Wigner-Seitz radius rs(rho_emb)' contains a duplicated 'the.'
  3. [SM Sec. SVIII] The cross-reference '(Lambda, Eq. ??,)' is a broken reference and should point to Eq. (2) of the main text.
  4. [SM Sec. SX] The error-evaluation paragraph describes the transition 'H2(nu=2,J=1 -> nu=2,J=1),' which appears to be a typo for nu=2 -> nu=1 as in Fig. S18.
  5. [Sec. III D / SM Fig. S15] The explanation that the disappearance of the Spiering-Meyer ODF/LDFA fingerprint arises from the Lambda_0-versus-Lambda approximation is plausible but is confounded by the simultaneous change of PES (static 6D versus full-dimensional moving-surface); a sentence acknowledging this confound would make the 'may be an artefact' claim properly qualified.
  6. [Sec. IV / Abstract] The claim that the models are 'the most accurate publicly available, full-dimensional models for H2 on copper to date' is not directly established by the benchmarks shown; recommend 'to our knowledge' or an explicit quantitative comparison with the alternative models cited in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dynamics are computed from DFT-fitted PES and EFT surrogates and checked against independent experimental observables.

full rationale

The central derivation chain is: (i) DFT electronic structure defines the PES and the ODF/LDFA friction tensors; (ii) ML models are fitted to those DFT labels; (iii) MDEF simulations integrate the Langevin equation; (iv) resulting sticking, survival, and transition probabilities are compared with experiment. None of the target observables (experimental sticking or survival probabilities) enters the fitting of either the MACE PES or the ACE-friction models. The PES database was refined against DFT-derived dynamical observables in Refs. 58/59, not against the experimental data used for validation, and the paper's own Figs. 5 and 6 provide an external check against measurements by Gostein et al. and Watts et al. The EFT surrogates are validated against held-out DFT friction data along trajectories and minimum energy paths; the weak-nonadiabaticity conclusion is a simulated result, not a restatement of the training labels. The ACE-friction representation is inherited from the authors' prior method paper [61], but the physical conclusion does not reduce to that citation: the method is a modeling framework, and the quantitative outcome is produced by the fitted models and the dynamics. The manuscript explicitly acknowledges local surrogate errors near the transition state (Section III A, Figs. S16 and S17); this is a robustness or correctness caveat, not a circularity. No equation is defined in terms of the quantity it is used to predict, and no fitted parameter is renamed as a prediction. Accordingly, no circular step is identified.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The central dynamics rest on fitted machine-learning potentials, fitted machine-learning friction tensors, and standard nonadiabatic approximations. No new physical entities or forces are introduced; the free parameters are ML coefficients and hand-set convergence and trajectory-analysis thresholds.

free parameters (7)
  • ACE density model coefficients = not reported
    Linear ACE coefficients fitted with Bayesian linear regression on 6,000 density labels for the LDFA electron density model.
  • ACE-friction model coefficients = not reported
    Linear coefficients optimized with the Adam optimizer and L2 regularization on roughly 1,984 ODF electronic friction structures.
  • Gaussian broadening width = 0.4 eV
    Chosen as a compromise for evaluating ODF friction; results are reported to be relatively insensitive between 0.3 and 0.8 eV.
  • Electronic temperature in Fermi factor = 300 K
    Used in Eq. 2 when evaluating ODF friction labels; affects the magnitude of the friction tensor.
  • ACE density model cutoff distance = 4 Angstrom
    Selected from cross-validation RMSE and convergence tests for the LDFA density surrogate.
  • ACE-friction model cutoff distance = 5 Angstrom
    Selected from RMSE versus cutoff tests; larger cutoffs degrade performance due to periodic cell overlap.
  • Dissociation criterion = H-H distance above 2.25 Angstrom
    Trajectories with H-H distance above this threshold are counted as dissociated; this hand-set threshold affects reported sticking probabilities.
assumptions (6)
  • domain assumption The MDEF Langevin equation with Markovian white-noise electronic friction is a valid description of weak nonadiabatic dynamics at metal surfaces.
    Invoked in Eq. 1 as the dynamical framework for all scattering and sticking simulations.
  • domain assumption The quasi-static limit of the orbital-dependent friction tensor, with Gaussian broadening, captures the physically relevant electron-hole-pair coupling.
    Eq. 2 is evaluated with a 0.4 eV Gaussian broadening and is central to all ODF friction labels.
  • domain assumption DFT with the SRP48 functional provides accurate enough potential energies, forces, and electronic friction labels for H2/Cu.
    The PES and ODF training data both come from FHI-aims DFT calculations using SRP48 and related settings described in Section II C.
  • domain assumption Classical nuclear dynamics with Einstein-Brillouin-Keller initial conditions can reproduce quantum-state-resolved scattering probabilities for H2.
    Initial rovibrational states are prepared with EBK quantization, and all reported scattering probabilities come from classical trajectories.
  • domain assumption The MACE PES from Refs 58 and 59 accurately represents the underlying DFT potential energy surface.
    The PES is the energy landscape for all BOMD and MDEF simulations; its accuracy is inherited from the prior publications.
  • domain assumption The row-wise coupling ansatz for the ACE-friction tensor preserves the required equivariance and positive semi-definiteness of the full friction tensor.
    The representation in Eq. 11 follows Ref 61 and is used for all ODF ACE-friction models.

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Pith. "Pith review of Nonadiabatic reactive scattering of hydrogen on different surface facets of copper." pith.science (2026). https://pith.science/paper/WMFIIAZP

@misc{pith2026250518147,
  author       = {Pith},
  title        = {Pith review of: Nonadiabatic reactive scattering of hydrogen on different surface facets of copper},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WMFIIAZP}},
  note         = {Machine review of arXiv:2505.18147}
}
abstract

Dissociative chemisorption is a key process in hydrogen-metal surface chemistry, where nonadiabatic effects due to low-lying electron-hole-pair excitations may affect reaction outcomes. Molecular dynamics with electronic friction simulations can capture weak nonadiabatic effects at metal surfaces, but require as input energy landscapes and electronic friction tensors. Here, we present full-dimensional machine learning surrogate models of the electronic friction tensor to study reactive hydrogen chemistry at the low-index surface facets Cu(100), Cu(110), Cu(111), and Cu(211). We combine these surrogate models with machine learning interatomic potentials to simulate quantum-state-resolved H$_2$ reactive scattering on pristine copper surfaces. The predicted sticking coefficient and survival probabilities are in excellent agreement with experiment. Comparison between adiabatic and nonadiabatic simulations reveals that the influence of electron-hole pair excitations on the scattering dynamics is weak and that the probability for dissociative adsorption is dominated by the shape of the underlying potential energy surface and the initial vibrational quantum state. Nonadiabatic effects only lead to subtle changes in rovibrationally inelastic state-to-state scattering probabilities. The differences between jellium-based isotropic local density friction and full ab-initio response-theory-based orbital-dependent friction are even more subtle. The presented machine learning models represent the most accurate publicly available, full-dimensional models for H$_2$ on copper to date.

Figures

Figures reproduced from arXiv: 2505.18147 by the authors.

Figure 1
Figure 1. FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
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Figure 2. FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
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Figure 3. FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
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Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
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Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
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Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
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Figure 7. Figure 7: FIG. 7 [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
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Figure 8. Figure 8: FIG. 8 [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
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Figure 9. Figure 9: FIG. 9 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.