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arxiv: 2605.04273 · v1 · submitted 2026-05-05 · ❄️ cond-mat.soft

Recognition: unknown

Polyamorphism in Glassy Network Materials

Max Hall-Brown, Peter Guy Wolynes

Pith reviewed 2026-05-08 17:27 UTC · model grok-4.3

classification ❄️ cond-mat.soft
keywords polyamorphismnetwork liquidsliquid-liquid transitionglass transitionnucleation kineticswater-like anomalies
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0 comments X

The pith

A simple model of network liquids, tuned to match water, shows the liquid-liquid phase transition near the glass transition proceeds via nanonucleation with tiny domains and nonclassical kinetics.

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

The paper constructs a microscopic model for network liquids that includes competition between bonded and nonbonded ordering, which produces polyamorphism with multiple distinct liquid phases. Parameters are adjusted to place a liquid-liquid transition in positions that resemble water's phase diagram, either above, near, or below the glass transition. Glass transition theory is then applied to calculate both the thermodynamics and the kinetics directly from the potentials. This combination shows that glassy dynamics reshape the transition process when it occurs near the glass temperature. Readers would care because the work connects familiar water-like anomalies in density to changes in how slowly the material relaxes.

Core claim

When the model parameters are tuned to produce a phase diagram resembling that of water, the liquid-liquid phase transformation near the glass transition temperature occurs via nanonucleation. This produces extremely small domain sizes and nonclassical nucleation kinetics.

What carries the argument

The microscopic potential balancing bonded and nonbonded ordering that generates the polyamorphic liquid-liquid transition, together with glass transition theory that determines how glassy dynamics modify the nucleation process.

If this is right

  • Thermodynamic anomalies such as a density maximum correspond to anomalies in the temperature dependence of glassy relaxation rates.
  • The position of the liquid-liquid transition relative to the glass transition can be shifted by changing microscopic parameters in the potential.
  • Glassy dynamics suppress classical nucleation and force the transition to occur through much smaller domains than expected without glassiness.
  • Polyamorphism can be made experimentally accessible by placing the transition either well above or well below the glass transition.

Where Pith is reading between the lines

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

  • This mechanism could account for why clear signs of a second liquid phase remain hard to detect in supercooled water.
  • The same small-domain nucleation might appear in other network glasses such as silica or chalcogenides when their transitions lie near their glass temperatures.
  • Varying the model parameters further could predict measurable changes in specific heat or viscosity that accompany the nanonucleation process.

Load-bearing premise

The tuned microscopic model captures the essential competition between bonded and nonbonded ordering in real network liquids and that glass transition theory applies without major changes to describe the kinetics and nucleation.

What would settle it

Direct measurement or simulation of domain sizes during the liquid-liquid transition in a network liquid held near its glass transition temperature, showing domains much larger than the nanoscale sizes predicted by the model.

Figures

Figures reproduced from arXiv: 2605.04273 by Max Hall-Brown, Peter Guy Wolynes.

Figure 1
Figure 1. Figure 1: FIG. 1. This schematic summarizes the library construction of the RFOT theory as it applies to a variety of materials. In the simplest case (left view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Spinodal points and coexistence curves for view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Here, we plot lines of constant configurational entropy, view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Near the critical point, the model features water-like anomalies. (a-b) The density shows a maximum and minimum with respect to view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Here, we plot (a) the glass transition temperature view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Here, we plot the dependence of the coexistence curves on view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Here, we plot the dependence of the coexistence curves on view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Here, we plot the dependence of the coexistence curves on view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Here, we summarize the behavior of the model when view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Here, we summarize the behavior of the model when view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Here, we summarize the behavior of the model when view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Here, we describe the protocol for calculating the surface tensions between liquid phases. (a) First, a Landau-Ginzberg like expansion view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Here, we plot the free energy barriers and timescales for several kinetic processes which may occur along pressurization protocols view at source ↗
Figure 14
Figure 14. Figure 14: FIG. 14. Here, we plot the free energy barriers and timescales for several kinetic processes which may occur along cooling protocols across the view at source ↗
Figure 15
Figure 15. Figure 15: FIG. 15. Here, we plot the free energy barriers and timescales for several kinetic processes which may occur along cooling protocols across view at source ↗
Figure 16
Figure 16. Figure 16: FIG. 16. Here, we plot (a, c) the free energy barriers and timescales and (b, d) the number of particles involved for several kinetic processes view at source ↗
Figure 17
Figure 17. Figure 17: FIG. 17. This plot is the analog of figure 4, for a material with view at source ↗
Figure 18
Figure 18. Figure 18: FIG. 18. Here, we show the results of applying self-consistent phonon theory to two realistic crystal structures which are favorable for view at source ↗
Figure 19
Figure 19. Figure 19: FIG. 19. Here, we show the typical behavior of nucleation kinetics in the vicinity of both view at source ↗
Figure 20
Figure 20. Figure 20: FIG. 20. Here, we summarize the behavior of the model when view at source ↗
read the original abstract

One dramatic feature of network liquids is the emergence at low temperatures and high pressures of polyamorphism, where multiple distinct liquid phases are accessed in a single material. Polyamorphism can arise from the competition between distinct local inherent structures corresponding to bonded and nonbonded ordering. Thermal bond breaking thus can lead to a phase transition often accompanied by thermodynamic anomalies away from the transition itself, such as the familiar density maximum in water at atmospheric pressure and $4^\circ$ C. Water exhibits network interactions in the form of hydrogen bonding between water molecules. The polyamorphic transition in water, however, is difficult to study due to the rapid crystallization of supercooled water and due to glassy effects at low temperatures. In the present work, we propose a simple microscopic model where the glassy and thermodynamic properties are both calculated directly from the microscopic potentials. The model contains a liquid-liquid phase transition, which, after tuning the microscopic parameters, may be located either above, near, or below the glass transition. By applying the Random First Order Transition theory of the glass transition to this simple microscopic model, we shine light on the interplay of polyamorphism and glassy properties in network liquids. We show the connection between the thermodynamic water-like anomalies and corresponding anomalies in the glassy kinetics. The analysis unveils key details on the way glassy dynamics modifies the phase transition kinetics. When the parameters of the model are tuned to produce a phase diagram resembling that of water, the liquid-liquid phase transformation near $T_g$ occurs via ``nanonucleation'', resulting in extremely small domains sizes and nonclassical nucleation kinetics which are predicted from the RFOT theory.

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 / 1 minor

Summary. The manuscript proposes a simple microscopic model for network liquids exhibiting polyamorphism from competition between bonded and nonbonded local ordering. Thermodynamic and glassy properties are stated to be calculated directly from the microscopic potentials. Model parameters are tuned so that the liquid-liquid phase transition (LLPT) lies above, near, or below the glass transition temperature Tg. Application of Random First Order Transition (RFOT) theory is used to link thermodynamic anomalies to kinetic ones and to predict that, for water-like tuning, the LLPT near Tg proceeds via nanonucleation, producing extremely small domain sizes and nonclassical nucleation kinetics.

Significance. If the central claims hold, the work would provide a useful theoretical connection between polyamorphism and glassy dynamics in network-forming materials, with implications for understanding anomalies in water and similar liquids. The direct computation from potentials and the derivation of nonclassical nucleation via RFOT are positive features. However, the parameter tuning to match a water-like diagram limits predictive power, and the assumption that standard RFOT applies without modification to the tuned model requires stronger justification to establish the claimed domain sizes and kinetics as robust predictions.

major comments (2)
  1. [Abstract] Abstract: The assertion that the LLPT near Tg 'occurs via nanonucleation' with 'extremely small domain sizes and nonclassical nucleation kinetics which are predicted from the RFOT theory' is obtained by inserting tuned parameters into unmodified RFOT expressions for the mosaic length scale and barrier. The text does not show that the configurational entropy drop or the surface tension term in the RFOT nucleation barrier have been recomputed from the microscopic potentials rather than assumed unchanged by the additional ordering channel.
  2. [Abstract] Abstract: Parameters are tuned to produce a phase diagram resembling that of water before the nanonucleation prediction is stated. This makes the location of the LLPT relative to Tg a fitted input rather than an output, so that the claimed sub-nm domains and modified kinetics depend on the chosen transition position and introduce moderate circularity in the argument.
minor comments (1)
  1. [Abstract] The abstract would be clearer if it briefly identified the form of the microscopic potentials (e.g., the functional form of the bonded and nonbonded interactions) rather than referring only to 'the microscopic potentials.'

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We address the major comments point by point below, providing clarifications on our calculations and agreeing to revisions that strengthen the presentation without altering the core results.

read point-by-point responses

  1. Referee: The assertion that the LLPT near Tg 'occurs via nanonucleation' with 'extremely small domain sizes and nonclassical nucleation kinetics which are predicted from the RFOT theory' is obtained by inserting tuned parameters into unmodified RFOT expressions for the mosaic length scale and barrier. The text does not show that the configurational entropy drop or the surface tension term in the RFOT nucleation barrier have been recomputed from the microscopic potentials rather than assumed unchanged by the additional ordering channel.

    Authors: We appreciate this observation. The configurational entropy drop is computed directly from the microscopic potentials through the model's enumeration of inherent structures and free-energy calculations, as outlined in the thermodynamic analysis sections. The surface tension term in the RFOT barrier is likewise obtained from the potential-derived mismatch penalty between bonded and nonbonded local orderings. We acknowledge that the manuscript could make these derivations more explicit in the abstract and main text to avoid any impression of unmodified insertion. In the revision we will add a concise paragraph detailing how both quantities are evaluated from the potentials for the polyamorphic case, confirming that the additional ordering channel is incorporated via the entropy term while the surface contribution remains consistent with the base RFOT framework. revision: partial

  2. Referee: Parameters are tuned to produce a phase diagram resembling that of water before the nanonucleation prediction is stated. This makes the location of the LLPT relative to Tg a fitted input rather than an output, so that the claimed sub-nm domains and modified kinetics depend on the chosen transition position and introduce moderate circularity in the argument.

    Authors: The tuning of microscopic parameters is an intentional capability of the model, enabling systematic exploration of LLPT positions above, near, or below Tg. For the water-like regime we select parameters that place the LLPT near Tg, after which RFOT theory yields the nanonucleation prediction as a direct consequence of the resulting mosaic length scale and barrier. This is not circular: the phase diagram is an output of the microscopic potentials, and the kinetic features follow from applying RFOT to that diagram. We agree, however, that the abstract should more clearly frame the result as a theoretical prediction for tuned network liquids rather than a universal claim. We will revise the abstract and introduction accordingly to emphasize the model's exploratory nature and the robustness of the nanonucleation outcome when the LLPT lies near Tg. revision: partial

Circularity Check

1 steps flagged

Tuned LLPT location near Tg followed by standard RFOT application yields nanonucleation as dependent outcome

specific steps
  1. fitted input called prediction [Abstract]
    "When the parameters of the model are tuned to produce a phase diagram resembling that of water, the liquid-liquid phase transformation near Tg occurs via ``nanonucleation'', resulting in extremely small domains sizes and nonclassical nucleation kinetics which are predicted from the RFOT theory."

    The relative position of the LLPT to Tg is fixed by parameter tuning to match water. The nanonucleation, sub-nm domains, and nonclassical kinetics are then obtained by direct substitution of those tuned parameters into standard RFOT expressions for mosaic length and barrier; the 'prediction' is therefore statistically forced by the tuning choice rather than derived anew from the microscopic potentials.

full rationale

The paper tunes microscopic model parameters to achieve a water-resembling phase diagram with the liquid-liquid phase transition (LLPT) positioned near Tg. It then applies unmodified RFOT formulas to obtain the nucleation mechanism, domain sizes, and nonclassical kinetics. This matches the 'fitted input called prediction' pattern because the claimed small domains and modified kinetics are direct consequences of the tuning choice (placing LLPT near Tg where RFOT length scales are small) rather than an independent first-principles computation of barriers or correlators from the potentials. The thermodynamic phase diagram itself is computed from the model, providing some independent content, but the headline kinetic prediction rests on the tuned location plus the assumption that RFOT applies without modification. No self-definitional equations, uniqueness theorems, or ansatz smuggling are exhibited in the provided text. Self-citation of RFOT is present but not load-bearing in a way that reduces the derivation to unverified prior claims. This yields moderate circularity (score 5) without full equivalence to inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The central claim depends on tuning several microscopic parameters to position the phase transition and on the applicability of RFOT theory; the model itself postulates competition between bonded and nonbonded local structures.

free parameters (1)
  • microscopic interaction parameters
    Tuned so the liquid-liquid transition lies above, near, or below the glass transition and so the overall phase diagram resembles water.
axioms (2)
  • domain assumption Random First Order Transition theory accurately describes the glassy dynamics and nucleation kinetics in the model
    Invoked directly to predict nanonucleation and kinetic anomalies without re-derivation in the abstract.
  • domain assumption The simple model captures the essential physics of polyamorphism via competition between bonded and nonbonded ordering
    Basis for the emergence of multiple liquid phases and thermodynamic anomalies.
invented entities (1)
  • nanonucleation no independent evidence
    purpose: Describes the mechanism of the liquid-liquid phase transformation near Tg with extremely small domain sizes
    Introduced as a consequence of RFOT applied to the tuned model; no independent experimental handle provided in the abstract.

pith-pipeline@v0.9.0 · 5587 in / 1732 out tokens · 29767 ms · 2026-05-08T17:27:25.858944+00:00 · methodology

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