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REVIEW 2 major objections 6 minor 30 references

A Dataset of Equilibrium State Configurations of Adsorption in Zeolites

T0 review · 2 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read AdsZeo records equilibrium methane configurations, not just averaged loadings, for 4,775 zeolite framework variants.

desk verdict Coordinate-resolved GCMC adsorption dataset that is genuinely new and internally validated; accept after fixing a text/figure mismatch and being clear about single-seed convergence limits. read the letter →

arxiv 2608.08848 v1 pith:FNA3PYAE submitted 2026-08-09 cond-mat.mtrl-sci

Marko Petković , Rachna Ramesh , Vlado Menkovski , Sofía Calero This is my paper · ORCID
classification cond-mat.mtrl-sci
keywords adsorptionzeolitesgrandcanonicalMonteCarlomethanemolecularconfigurationscoordinate-resolveddatasetmachinelearningsodiumcations
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

AdsZeo is a coordinate-resolved dataset of equilibrium methane adsorption configurations in aluminium-substituted, sodium-containing zeolites. It saves the actual Monte Carlo coordinates of every methane pseudo-atom and mobile sodium cation at 13 pressures between 0.1 and 100 bar, alongside per-frame energy and loading statistics, rather than only ensemble-averaged isotherms. The paper argues that this configuration-level archive, spanning 191 zeolite topologies and 4,775 framework realisations from 62,075 grand canonical Monte Carlo simulations, fills a gap left by high-throughput adsorption databases that discard molecular configurations. If the saved frames are genuine equilibrium samples, the dataset provides a training substrate for machine-learning models that generate adsorption configurations or density fields in charged zeolite pores.

What carries the argument

The load-bearing mechanism is a coordinate-preserving grand canonical Monte Carlo protocol: each framework realisation is equilibrated for 5,000 cycles and run for 200,000 production cycles, with one full configuration frame saved every 1,000 cycles. Each saved frame stores Cartesian coordinates of united-atom methane pseudo-atoms and mobile sodium cations together with per-frame counts and energy components, and all frames are linked by run and structure identifiers to scalar isotherm records in a processed database. This pairing of coordinates with scalar averages is what lets the dataset be checked for consistency and what makes it usable for configuration-level machine learning.

What would settle it

Rerun the state point with the largest reported block deviation, 7.935 methane per unit cell, from new random seeds and compare the per-frame distribution of methane counts and energies; if the new runs do not fall within the reported block statistics, the equilibrium-equivalence claim for that framework realisation fails.

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Extended reading notes

Core claim

The central claim is that AdsZeo provides coordinate-resolved adsorption data across variations in framework topology, aluminium content and distribution, sodium cation arrangement, pressure, and methane loading. The processed release contains 12,415,000 saved production frames and 1,245,376,215 saved particle-coordinate records. The paper validates these records by showing that loadings reconstructed from saved frames agree with the simulation software's scalar loadings to a mean absolute deviation of 0.1794 methane per unit cell, that block statistics show no systematic drift, that pair-distance checks show no unphysical overlaps, and that all isotherms increase monotonically with pressure. External comparisons with experimental methane isotherms for three sodium-exchanged zeolites reproduce the overall uptake trends, supporting the claim that the stored configurations are realistic equilibrium samples.

Load-bearing premise

The force field and the single-run GCMC protocol produce true equilibrium ensembles for every one of the 4,775 framework realisations, including those with aluminium distributions and Al-O-Al environments the parameters were not specifically fitted to.

Editorial extensions

If this is right

  • Machine-learning models can be trained to generate full adsorption configurations in charged zeolite pores, not just to predict scalar loadings.
  • Spatial statistics and density estimation of methane and sodium cation distributions become possible across 191 topologies and a 0.1 to 100 bar pressure range.
  • The saved frames can be used as equilibrium samples in benchmarks for configuration generation, provided data splits respect framework topology and run identity.
  • Zero-loading frames are retained as valid equilibrium samples, preserving the low-pressure onset of adsorption for modelling.
  • Because each topology has 25 aluminium-substitution realisations, the dataset supports studying how aluminium distribution and cation arrangement affect local adsorption geometry.

Reading between the lines

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

  • The saved mobile sodium coordinates make the database a potential resource for studying how cation positions respond to methane loading, a coupling the paper notes but does not itself analyse.
  • The force field is validated experimentally on only a few framework compositions, so a natural extension is targeted validation on additional aluminosilicate frameworks, especially those containing Al-O-Al environments.
  • Users modelling low-loading regimes should weigh the reported block-deviation statistics, with a largest absolute deviation of 7.935 methane per unit cell, when deciding which runs are converged enough for their task.
  • Coordinate-resolved datasets of this kind could be used to train conditional generative samplers that combine pressure, topology, and cation arrangement as inputs, potentially reducing the cost of future GCMC screening.
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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

2 major / 6 minor

Summary. The paper presents AdsZeo, a coordinate-resolved dataset of grand-canonical Monte Carlo (GCMC) methane adsorption configurations in aluminium-substituted, sodium-exchanged zeolites. The authors generate 4,775 framework realisations from 191 IZA topologies, simulate each at 13 pressures between 0.1 and 100 bar at 298 K with RASPA, and store scalar adsorption records (isotherms, frame-level energy/counts) together with saved production-frame Cartesian coordinates of methane pseudo-atoms and Na+ cations in a DuckDB database. The paper describes the structure-generation workflow, the assembled force field, the GCMC protocol, the database schema, and four internal validation checks (block statistics, coordinate-derived loading consistency, pair-distance geometry, monotonicity of isotherms) plus a comparison with experimental methane isotherms for Na-ZSM-5, NaY, and NaX. The central claim is that the released coordinates represent equilibrated GCMC samples across a broad range of framework chemistry and pressure, suitable for training machine-learning models for configuration generation.

Significance. If the dataset meets its claims, it fills a genuine gap in the high-throughput adsorption literature, which has mostly archived scalar properties; the coordinate-level records would enable generative and density-estimation models for charged nanoporous hosts. The paper's internal validation is appropriately matched to the dataset claim: the coordinate-derived loadings reconstruct the RASPA scalar loadings with a mean absolute deviation of 0.18 CH4 per unit cell, no short-range coordinate artefacts are reported, all isotherms are monotonic, and the dataset and generation code are publicly available. The main risk to the equilibrium claim is the per-run convergence tail documented in Section 4.1, which is disclosed but not resolved at the level of individual runs. On balance, the resource is valuable and the weaknesses are largely addressable.

major comments (2)
  1. [§2.3, §4.1] The paper labels all 62,075 runs as equilibrium samples, but each run used a single random seed and the block statistics in Section 4.1 show a non-negligible tail of runs with drift: the 95th percentile absolute relative block deviation is 5.26% and the largest absolute block deviation is 7.935 CH4 per unit cell. Because the central claim is that the saved coordinates are equilibrium configurations, the release should include a per-run convergence diagnostic (e.g., block-deviation values or a pass/fail flag) and the text should state the fraction of runs satisfying a defined convergence criterion; otherwise the 'equilibrium' label is not verifiable for the outlier runs.
  2. [§2.2, §4.5] The force field assembled from Refs. 24-26 is applied to all 191 topologies, including Al-O-Al environments, but the external validation in Section 4.5 covers only Na-ZSM-5, NaY, and NaX. The paper should either provide additional transferability evidence for representative topologies outside these three systems or explicitly state that the 'realistic methane adsorption behaviour' claim at the end of Section 4.5 is established only for the tested systems and is a transferability assumption elsewhere.
minor comments (6)
  1. [§4.1, Figure 4] The body text gives example mean methane counts of 32.3, 50.5, and 60.0 CH4 for the 1, 10, and 100 bar traces, while the Figure 4 labels show 36.0, 86.8, and 120.9 CH4; these numbers must be reconciled so that the primary production-stability figure is self-consistent.
  2. [§2.1, §4.5] Section 2.1 states that Löwenstein's rule was not explicitly enforced, while Section 4.5 says Al substitutions were generated 'subject to the non-Loewenstein constraint'; please clarify whether this means the same thing, since the phrase 'non-Loewenstein constraint' could be read as enforcing absence of Al-O-Al rather than allowing it.
  3. [§4.4] There is a typo in 'near-zere methan loading'; it should read 'near-zero methane loading'.
  4. [§4.1] The sentence 'do not show a appreciable, systematic first-to-last-block drift' contains a grammar error ('a appreciable'); it should read 'no appreciable systematic drift'.
  5. [Figure 2, Table 3] Figure 2 lists 'Structures / isotherms 4,775' while Table 3 reports 1,489,800 rows in the isotherms table; please clarify that the former counts distinct isotherms while the latter counts scalar records, to avoid an apparent inconsistency.
  6. [§2.3] The 13 pressures are only described as 'logarithmically spaced between 0.1 and 100 bar'; for reproducibility, please list the exact pressure values or point to where they are stored in the database schema.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the dataset claim rests on an inherited force field and GCMC runs with clearly disclosed validation limits; no prediction reduces to a fit or to the dataset's own outputs.

full rationale

AdsZeo makes no parameter fit and derives no quantitative result from its own outputs. Its central claim is that the released coordinates are equilibrated GCMC samples under the stated force field. The force field is assembled from Refs. 24-26, which are prior published parameter sets by overlapping groups; the paper does not refit them, and those references carry their own external validation, so this is independent support rather than circularity. The block-stability checks (Sec. 4.1) and coordinate-vs-scalar consistency check (Sec. 4.2) are internal consistency validations of parsing and stationarity, not predictions derived from the data they validate; the paper explicitly frames Sec. 4.2 as consistency 'rather than identity between two independently stored quantities.' The experimental comparison (Sec. 4.5) is presented as an external consistency check, and the paper discloses that quantitative agreement is not expected because simulated structures are generated from ideal frameworks. No uniqueness theorem, ansatz, or fitted-parameter prediction is invoked. The paper itself discloses the main validity limits: single-seed runs (Sec. 2.3), the largest block deviation of 7.935 CH4/uc and the 4 CH4/uc cutoff for relative statistics (Sec. 4.1), and transferability assessed with only three experimental comparisons (Sec. 4.5). These affect evidence strength, not circularity. One non-circular consistency issue should be corrected: Sec. 4.1 gives example means 32.3/50.5/60.0 CH4 while Fig. 4 labels show 36.0/86.8/120.9; the figure and text need reconciliation, but this does not indicate that any claimed result reduces to its inputs.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The dataset rests on a published force field, standard GCMC, and hand-chosen protocol values; no parameters were fitted to the validation data. The listed design choices and domain assumptions are the ones a replicator would have to accept to trust the 'equilibrium' label.

free parameters (5)
  • Al substitution count per topology = interpolated from 1 Al to about 25 percent of tetrahedral sites
    Section 2.1; hand-chosen compositional sweep. Not fitted to data, but sets the charge-balancing Na+ count for every realisation.
  • Number of framework realisations per topology = 25
    Section 2.1; chosen to balance diversity with computational cost.
  • Supercell lattice-vector threshold = all lattice vectors longer than 24 angstroms
    Section 2.1; satisfies minimum-image convention for the 12.0 angstrom cutoff.
  • Production length and saving interval = 200,000 production cycles; one saved frame per 1,000 cycles
    Section 2.3; assumed sufficient for convergence and validated only by block averages, not by repeated runs.
  • Pressure grid = 13 log-spaced pressures from 0.1 to 100 bar
    Section 2.3; coverage of the adsorption isotherm, but arbitrary in spacing.
assumptions (5)
  • domain assumption Framework atoms are rigid during adsorption.
    Section 2.1 and 2.2; real zeolites can flex, and flexibility can affect adsorption at high loading. The dataset is explicitly for rigid frameworks.
  • domain assumption The force field parameters from references 24-26 transfer to all 191 topologies, including Al-O-Al environments.
    Section 2.2; parameters were developed on a limited set of zeolites. Experimental validation covers only three sodium zeolites.
  • domain assumption 200,000 production cycles without repeated seeds are sufficient for equilibration in every run.
    Section 2.3 and 4.1; block checks support stationarity on average, but the largest block deviation is 7.935 CH4 per unit cell.
  • domain assumption Loewenstein's rule is not enforced, so Al-O-Al environments are allowed.
    Section 2.1; follows reference 24 but may create aluminium distributions unlike real synthesis products.
  • domain assumption United-atom methane and point-charge Na+ are adequate representations for the intended machine-learning training purpose.
    Section 2.2; standard coarse-graining from cited force fields.

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Cite this review

Pith. "Pith review of A Dataset of Equilibrium State Configurations of Adsorption in Zeolites." pith.science (2026). https://pith.science/paper/FNA3PYAE

@misc{pith2026260808848,
  author       = {Pith},
  title        = {Pith review of: A Dataset of Equilibrium State Configurations of Adsorption in Zeolites},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FNA3PYAE}},
  note         = {Machine review of arXiv:2608.08848}
}
abstract

Zeolites are crystalline nanoporous materials widely used in adsorption, separation, and catalytic processes. Molecular simulations are commonly used to predict adsorption properties, but most high-throughput adsorption datasets report only ensemble-averaged quantities such as loadings or isotherms, rather than the molecular configurations from which these averages are obtained. Here, we present AdsZeo, a coordinate-resolved dataset of equilibrium methane adsorption configurations in aluminium-substituted, sodium-containing zeolite frameworks. The processed release contains 4,775 framework realisations derived from 191 zeolite topologies. Each framework realisation was simulated at 13 methane pressures between 0.1 and 100 bar at 298 K using grand canonical Monte Carlo simulations, giving 62,075 production simulations in total. In addition to scalar adsorption records, the dataset stores production-frame methane pseudo-atom coordinates, mobile Na$^+$ cation coordinates, framework atomic coordinates, per-frame loading and energy statistics, and simulation metadata in a processed DuckDB database. The release contains 12,415,000 saved production-frame records and 1,245,376,215 saved particle-coordinate records. AdsZeo provides coordinate-resolved adsorption data across variations in framework topology, aluminium content and distribution, sodium cation arrangement, pressure, and methane loading, enabling reuse for adsorption analysis, spatial statistics, density estimation, and machine-learning models for molecular configuration generation in charged zeolite pores.

Figures

Figures reproduced from arXiv: 2608.08848 by the authors.

Figure 1
Figure 1. Spatial distribution of methane molecules and sodium cations within a mordenite zeolite [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Schematic overview of the dataset creation process and the main archived output scales. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Relational structure of the processed database. Structure identifiers link framework metadata [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Monte Carlo production stability. (a) Representative methane-count traces for simulations at [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Consistency between saved coordinate records and scalar adsorption records. (a) Parity plot [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Geometric validation of saved configurations. Shell-normalised minimum-image pair-distance [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Pressure-dependent methane loading distribution across the dataset. Thin grey lines show [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Comparison between simulated and experimental methane adsorption isotherms for selected [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

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