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arxiv: 2606.01401 · v1 · pith:VVUW35ATnew · submitted 2026-05-31 · ❄️ cond-mat.mtrl-sci

Flat interface between amorphous ices and the role of MDA-like intermediate states in the LDA-HDA transformation

Anastasiia Shupletsova , Vladimir Stegailov This is my paper

Pith reviewed 2026-06-28 16:35 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci
keywords amorphous iceLDAHDAMDALDA-HDA interfacepolyamorphismSOAP descriptorsmolecular dynamics
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0 comments X

The pith

MDA-like intermediate states in the LDA-HDA ice transition localize at the flat interface rather than forming a bulk phase

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

The paper examines the microscopic mechanism of the pressure-driven switch between low-density and high-density amorphous ice. It identifies a stable flat boundary between the two and ties medium-density amorphous ice-like arrangements to that boundary. Molecular dynamics trajectories combined with a neural network that reads SOAP descriptors show the intermediate configurations stay confined to the interface layer. The layer keeps fixed thickness while sliding back and forth with pressure, producing reversible elastic motion and hysteresis on release.

Core claim

The first direct characterisation of a flat LDA||HDA interface reveals an equilibrium thickness that stays constant under compression while the interface position shifts reversibly into the LDA region. This elastic response displays kinetic hysteresis on decompression. MDA-like intermediate amorphous configurations are not a distinct bulk phase but localise at the LDA-HDA interface, as mapped by the neural network classifier that uses orientational information from the hydrogen-bond network.

What carries the argument

Neural network classifier trained on SOAP descriptors that labels local molecular environments as LDA, HDA or MDA-like, applied to track the spatial distribution of structures across the flat LDA-HDA interface in molecular dynamics trajectories.

If this is right

  • The interfacial layer thickness remains constant under compression.
  • The interface position shifts reversibly into the LDA region under increasing pressure.
  • The system exhibits kinetic hysteresis upon decompression that resembles memory effects.
  • Orientational information from the hydrogen bond network is required for accurate local discrimination of the amorphous ices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If MDA-like states are strictly interfacial, then medium-density amorphous ice may be re-interpreted as a boundary phenomenon rather than an independent metastable phase in this polyamorphic transition.
  • The same interface-localization pattern could appear in pressure-driven transitions of other network-forming liquids.
  • Larger-scale simulations or direct structural probes at the interface could test whether the constant thickness persists when the LDA and HDA domains are macroscopic.

Load-bearing premise

The neural network classifier based on SOAP descriptors reliably distinguishes LDA, HDA, and MDA-like local environments in the molecular dynamics trajectories.

What would settle it

Observation of MDA-like configurations distributed throughout the interior of LDA or HDA domains away from the interface in independent simulations would show they can exist as bulk states.

Figures

Figures reproduced from arXiv: 2606.01401 by Anastasiia Shupletsova, Vladimir Stegailov.

Figure 1
Figure 1. Figure 1: FIG. 1: SOAP descriptor construction (left) followed by the neural network classification pipeline (right). Each water molecule [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: Confusion matrices for a) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3: Distributions of sample data in principal component space. The top panel shows the results for model [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: a) The dependence of the fraction of LDA, HDA, and IA phases on pressure during isothermal compression of LDA [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5: Snapshots of 3.0 Å-thick slices of the computational cell along the plane [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6: Evolution of HDA and IA clusters during [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7: Top panel: snapshot of the LDA || HDA || LDA system after relaxation at 77 K, pressure equal to 0.2 GPa. Oxygen [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8: Distributions of the projected atomic environments in PC space. Training data (LDA, MDA, HDA) are shown together [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9: Probability profile at different stages of compression [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 6
Figure 6. Figure 6: This confirms that the presence of an interphase layer [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

The pressure-induced transformation between low-density amorphous ice (LDA) and high-density amorphous ice (HDA) is a prototypical polyamorphic transition, yet its microscopic mechanism - and in particular the nature of intermediate amorphous (IA) states - has long remained unresolved. We present the first characterisation of a flat LDA||HDA interface, determining its equilibrium thickness and showing its link to medium-density amorphous ice (MDA). Interestingly, the thickness of the interfacial layer remains constant under compression, while its position shifts reversibly into the LDA region - an elastic response that exhibits kinetic hysteresis upon decompression, reminiscent of memory effects. For high-precision discrimination of amorphous ices, we use a neural network classifier based on SOAP (Smooth Overlap of Atomic Positions) descriptors. The systematic description of the local structure emphasizes the importance of orientational information from the hydrogen bond network. Furthermore, this approach enables molecular dynamics analysis of the LDA-to-HDA transformation, directly mapping the spatial distribution of intermediate structures relative to growing HDA domains and the surrounding LDA matrix, and revealing that MDA-like IA configurations are not a distinct bulk phase but rather localise at the LDA-HDA interface.

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

1 major / 1 minor

Summary. The manuscript uses molecular dynamics simulations to characterize a flat LDA||HDA interface in amorphous ice under pressure, reporting that its thickness remains constant while its position shifts reversibly into the LDA region (with hysteresis on decompression). It employs a neural network classifier trained on SOAP descriptors to map local environments and concludes that MDA-like intermediate amorphous (IA) configurations are not a distinct bulk phase but instead localize at the LDA-HDA interface.

Significance. If the spatial mapping of local structures is reliable, the work offers a concrete microscopic picture of the LDA-HDA polyamorphic transition and the role of interface-localized intermediates, which could inform models of amorphous ices and related polyamorphic systems. The application of SOAP-based machine learning to resolve orientational features of the hydrogen-bond network in MD trajectories is a methodological strength.

major comments (1)
  1. [Abstract (and Methods section describing the NN classifier)] The central claim that MDA-like IA states localize at the interface (rather than constituting a bulk phase) rests entirely on the spatial distribution obtained from the SOAP-based neural network classifier. No information is given on training-set composition, cross-validation accuracy, confusion matrix, or robustness against mixed or transitional environments at an interface; without these, it is impossible to rule out that the observed localization is an artifact of how the classifier treats averaged or boundary configurations.
minor comments (1)
  1. [Abstract] The abstract states that the interfacial layer 'remains constant under compression' yet 'its position shifts reversibly'; a brief clarification of the precise definition of thickness versus position would aid readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive assessment of our work and for identifying the need for additional validation details on the neural network classifier. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract (and Methods section describing the NN classifier)] The central claim that MDA-like IA states localize at the interface (rather than constituting a bulk phase) rests entirely on the spatial distribution obtained from the SOAP-based neural network classifier. No information is given on training-set composition, cross-validation accuracy, confusion matrix, or robustness against mixed or transitional environments at an interface; without these, it is impossible to rule out that the observed localization is an artifact of how the classifier treats averaged or boundary configurations.

    Authors: We agree that the manuscript does not currently provide sufficient details on the training-set composition, cross-validation accuracy, confusion matrix, or robustness tests for the SOAP-based neural network classifier. These elements are necessary to fully support the reliability of the spatial mapping and to address potential concerns about classification artifacts at interfaces. In the revised manuscript we will expand the Methods section to include: the composition and size of the training set (pure LDA, HDA and MDA configurations), cross-validation accuracy, the confusion matrix, and explicit tests on mixed or transitional environments generated at model interfaces. These additions will allow readers to assess whether the observed localization of MDA-like states is robust. We do not anticipate that providing this information will change the main conclusions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on standard external descriptors

full rationale

The abstract and description present the central claim (MDA-like states localize at the LDA-HDA interface) as arising from MD trajectory analysis via a neural network classifier on standard SOAP descriptors. No equations, self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations appear. The classifier is described as using established SOAP methods without any indication that its training or outputs are constructed from the target result itself. This meets the default expectation of a self-contained analysis against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no free parameters, axioms, or invented entities can be extracted or audited.

pith-pipeline@v0.9.1-grok · 5747 in / 921 out tokens · 22874 ms · 2026-06-28T16:35:46.580164+00:00 · methodology

discussion (0)

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Reference graph

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