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REVIEW 4 major objections 6 minor 83 references

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read By generating MOFs block by block, a diffusion model reaches 1,000-atom unit cells and proposes building blocks never seen in its training data.

desk verdict A useful building-block-aware diffusion framework for MOFs, with a real synthesis step, but the headline validity numbers inherit the curation cutoffs and the code link underdelivers. read the letter →

arxiv 2505.08531 v1 pith:PD5BAVIR submitted 2025-05-13 physics.chem-ph cond-mat.mtrl-scics.LG

Chenru Duan , Aditya Nandy , Sizhan Liu , Yuanqi Du , Liu He , Yi Qu , Haojun Jia , Jin-Hu Dou This is my paper · ORCID
classification physics.chem-phcond-mat.mtrl-scics.LG
keywords metal-organicframeworksgenerativediffusionmodelsbuilding-blockrepresentationSE(3)equivarianceequivariantgraphneuralnetworkstopologicalnetsdenovomaterialsdesignCoREMOF
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 tries to establish that a generative model can design new metal–organic frameworks (MOFs) by learning the three-dimensional shapes of their building blocks — inorganic nodes, organic edges, and topological nets — rather than by recycling known blocks or generating entire unit cells at once. If true, de novo MOF design can produce crystals with unit cells of roughly 1,000 atoms, a size all-atom diffusion has not previously reached, and can propose metal clusters and linkers absent from the training database. The authors report 52% geometric validity among assembled MOFs, 27% novelty, and diversity that overlaps experimental databases, and they synthesize one predicted MOF, confirming its overall framework by powder X-ray diffraction, thermogravimetric analysis, and nitrogen sorption.

What carries the argument

The central machinery is the building-block representation itself, coupled with an object-aware $\mathrm{SE}(3)$-equivariant diffusion framework. A MOF is disassembled into an inorganic node, an organic edge, and a topological net; the diffusion model denoises the all-atom Cartesian coordinates of each block while keeping atom types fixed, and the denoising network is a LEFTNet equivariant graph neural network adapted from the authors' earlier object-aware reaction diffusion model. Assembly is performed by PORMAKE, and conditional design is implemented by inpainting, which keeps known nodes or nets fixed while denoising only the target component. The reduction in graph size is what lets the model scale to unit cells of roughly 1,000 atoms.

What would settle it

Re-validate the generated MOFs using a lower edge-angle cutoff (e.g., 120 degrees) and a relaxed node RMSD (e.g., 0.5 Å), and count how many of the 52% valid structures remain valid; if the fraction drops sharply, the validity claim is an artifact of the curation thresholds. A second decisive test is to synthesize another high-scoring generated MOF with a novel node or edge and check whether the experimentally obtained structure matches the predicted framework, since the one successful synthesis involved a model structure whose solvent coordination differed from the experiment.

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Extended reading notes

Core claim

BBA MOF Diffusion is an $\mathrm{SE}(3)$-equivariant denoising diffusion model that represents a MOF as a joint distribution over three objects: one inorganic node, one organic edge, and a topological net. Trained on the CoRE MOF 2019 database of experimentally synthesized MOFs, the model samples new all-atom nodes and edges and assembles them, via PORMAKE, onto one of four simple nets (dia, nbo, sra, pcu). The central claim is that this native building-block representation produces unprecedented metal nodes and organic edges, expanding accessible chemical space by orders of magnitude, while still yielding assembled MOFs with good geometric validity: 52% of sampled MOFs are valid, 27% are novel, 13% are both novel and unique, and unit cells range from 37 to 904 atoms. The synthesized [Zn(1,4-TDC)(EtOH)2] MOF demonstrates that at least one generated structure corresponds to a real material, with the experimental structure differing from the model's prediction in the coordination of ethanol solvent molecules.

Load-bearing premise

The load-bearing premise is that the curation cutoffs used to build the training set — a 0.3 Å root-mean-square deviation for node–net compatibility and a 140-degree minimum angle between an edge's connecting points — define which building blocks are genuinely valid, so that the reported 52% validity reflects synthesizability rather than the curation rule itself; the paper concedes that some valid MOFs in CoRE have edge angles below 140 degrees.

Editorial extensions

If this is right

  • Generative MOF design no longer needs to recycle known building blocks: the model can propose previously unseen metal nodes and organic edges, shifting the bottleneck from structure generation to synthesis planning.
  • Unit cells containing up to roughly 1000 atoms can be sampled, so all-atom generation can cover realistic MOF crystals rather than only small model systems.
  • Because the joint distribution is learned from experimentally synthesized MOFs, generated candidates resemble the diversity of CoRE MOF while adding novelty, making them plausible targets for experimental follow-up.
  • Conditional generation by inpainting lets chemists fix a known node and net and request linkers with a specified chemical composition, giving a practical workflow for linker-focused design.
  • At least one model-predicted MOF, [Zn(1,4-TDC)(EtOH)2], was synthesized and characterized, demonstrating that the pipeline can output a real, crystallographically confirmed material.

Reading between the lines

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

  • A direct stress test of the validity claim would be to retrain or re-sample with the edge-angle cutoff lowered from 140 to about 120 degrees and re-measure validity; if the model's building-block geometries are genuinely sound, validity should degrade only mildly.
  • The model currently generates only the four most common nets and does not invent new topologies; treating the net itself as a diffused object, or sampling nets from a generative model of the long tail, would be the natural next step and is not tested in this paper.
  • Because the diffusion model keeps atom types fixed and only denoises coordinates, the chemistry of generated building blocks is bounded by the atomic compositions the user supplies; coupling this model with a composition generator would close the loop to fully automatic design.
  • The same object-aware, building-block treatment could transfer to other modular molecular systems where one component's identity and another's coordinates interact without direct 3D contact, such as co-crystals or protein–ligand complexes, although the paper does not demonstrate those cases.
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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

4 major / 6 minor

Summary. The manuscript introduces BBA MOF Diffusion, an SE(3)-equivariant diffusion model that generates MOF crystals from a building-block representation consisting of an inorganic node, an organic edge, and a topological net, with the joint distribution learned from CoRE MOF 2019. Building blocks are denoised independently and assembled with PORMAKE on four nets (dia, nbo, sra, pcu). The authors report 63% edge validity, 82% node validity, and 52% overall geometric validity among 9,712 generated MOFs, unit cells up to 904 atoms, 27% novelty (13% novel and unique), conditional edge generation via inpainting, and an experimental synthesis of [Zn(1,4-TDC)(EtOH)2] characterized by PXRD, TGA, and N2 sorption. The paper candidly discusses its limitations: only single-node, single-edge MOFs and four nets are treated, property-guided generation is not yet implemented, and chemical compositions are assumed known.

Significance. If the claims hold, the paper represents a meaningful advance: representing MOFs as jointly diffused building blocks decouples the all-atom graph size handled by the scoring network from the unit-cell atom count, scaling generation to roughly 900 atoms, and the model can produce novel nodes and edges rather than recombining known fragments. The open code and data availability, the explicit statement of the four-nets limitation, and the attempt at external experimental validation with PXRD, TGA, and BET data are concrete strengths, and an experimental anchor of this kind is rare in generative-model papers. The significance is contingent on whether the reported validity numbers mean anything beyond the curation thresholds used to construct the training set, which is the central concern raised below.

major comments (4)
  1. [Section 2b / Section 4b] The headline validity numbers are not independent of the curation procedure. Edge validity is defined by the same 140–180 degree connecting-point angle criterion used to curate the training set, and node validity is defined by the same <0.3 Å net-compatibility RMSD criterion described in Section 4b. The paper itself notes in Section 2b that edges with angles below 140 degrees (e.g., sulfonyldibenzene) occur in CoRE MOF and can form valid MOFs, so the 63%/82%/52% figures conflate 'compatible with the curation heuristics' with 'geometrically plausible and synthesizable'. Passing the filters does not imply a synthesizable structure, and failing them does not imply an invalid one; the abstract's phrase 'great geometric validity' is therefore not supported as an independent measure of physical plausibility. I recommend re-evaluating a subset of generated MOFs with an external structure-sanity check (for example, local coordination-environment validation or geometry relaxation) and/or reporting the same validity statistics for a non-generative baseline so the reader can calibrate the 52% figure.
  2. [Abstract and Section 2d] The abstract states that PXRD, TGA, and N2 sorption 'confirm its structural fidelity', but Section 2d reports that the synthesized structure differs from the model prediction: two TDC ligands predicted to coordinate to Zn are actually replaced by ethanol, and the refined structure is a trinuclear SBU with MIL-53 (2D triangular) topology. The experiment therefore validates a MOF that is similar to, and inspired by, the model prediction, rather than the predicted structure itself. This distinction matters for a paper whose central claim includes a practical pathway to synthesizable MOFs, and it should be reflected in the abstract. In addition, the statement that the simulation 'fits well' with the PXRD data could be strengthened by reporting the refinement residuals (for example, Rwp and Rp).
  3. [Section 2a vs Sections 2b/4b] The edge-angle cutoff is stated inconsistently. Section 2a says edges with pairwise connecting-point angles of at least 120 degrees were used, while Sections 2b and 4b report a 140–180 degree constraint inherited from prior work and use that constraint to define edge validity. Because the distinction between curation criteria and validity criteria is central to the paper's quantitative claims, the applied cutoffs for training-data curation and for validity must be stated consistently; if two different cutoffs were in fact used, their relationship needs to be explained.
  4. [Section 2c / Section 2b] The claims that generated MOFs 'faithfully represent' CoRE MOF and show 'highly similar distributions' of edge angles and node RMSDs (Figs. 2d and 3) are supported only by visual overlay, and the distribution comparison in Fig. 2d is computed on valid samples that are filtered by exactly the same 140-degree and 0.3 Å thresholds used to define validity. No quantitative distance measure (such as an RAC-space overlap, a KL divergence, or nearest-neighbor statistics) is reported, and no validity or novelty comparison against MOFDiff, MOFFlow, or another MOF generative baseline is provided. Without such comparisons, the abstract's claim that the model 'readily samples MOFs with unit cells containing 1000 atoms with great geometric validity, novelty, and diversity mirroring experimental databases' cannot be benchmarked against existing methods.
minor comments (6)
  1. [Abstract / Fig. 2c] Fig. 2c shows a maximum of 904 atoms in the unit cell, while the abstract and Section 2b mention '1000 atoms' and 'approximately 1000 atoms'; the numbers should be aligned.
  2. [Section 2b] With 20% novel edges and 25% novel nodes among valid MOFs, a MOF containing at least one novel building block should occur more often than 25% under independence; the reported 27% overall novelty implies strong co-occurrence of novel edges and novel nodes (or a difference in the base sets over which the rates are computed), and this should be explained or clarified.
  3. [Section 2d] There are several typos in Section 2d, including 'tow carboxylates', 'An hexagonal node', and 'The N2 Adsorption-desorption Analysis was also experiment to obtained the BET surface areas', which should all be corrected.
  4. [Section 4a] The simplified training objective L_simple is written without the expectation over the data and noise distributions; the standard form with the expectation should be restored.
  5. [Fig. 2e] The conditionally generated edges in Fig. 2e are described qualitatively as 'reasonable'; a quantitative validity check for the inpainting products would strengthen the conditional-generation claim.
  6. [Fig. 2d] The edge-angle comparison between generated and CoRE MOF samples should either include invalid generated MOFs or explicitly note the truncation at the validity cutoff in the caption, since the present plot compares filtered generated samples against an unfiltered experimental distribution.

Circularity Check

2 steps flagged · score 6.0 of 10

Validity metric is inherited from the curation cutoffs; reported 52%/63%/82% validity partly restates training-set filters rather than an external synthesizability criterion.

  1. fitted input called prediction [Section 2b (validity statistics) vs. Section 4b (dataset curation)]
    "A MOF is categorized as valid only if all building blocks are valid, resulting 52% rate of overall validity for generated MOFs. ... Using a cutoff of 0.3 Å, as in prior work, we determined compatibility of inorganic node building blocks with topological nets. We initially determined organic building blocks using a 140◦cutoff angle."

    The validity statistics are computed by reapplying the same geometric filters that defined the training set: node RMSD <0.3 Å on a net and edge connecting angle ≥140° (Section 4b). Because the training data were selected by exactly these cutoffs, the reported 63% edge validity, 82% node validity, and 52% overall validity are not independent measures of synthesizability; they partly restate the curation rule. The paper concedes the rule is not a true validity condition: 'an edge could still form a valid MOF in spite of its acute angle, with a salient example being sulfonyldibenzene ... angle ... <140 degree.' Thus the abstract's 'great geometric validity' inherits the training filter by construction.

  2. ansatz smuggled in via citation [Section 2b, paragraph on sources of invalidity]
    "This is mostly caused by a tight constraint on the connecting points angle between 140 and 180 degree, which is inherited from a previous work, mimicking an ideal linear arrangement for the two connecting points in an edge (Supplementary Figure 1)."

    The 140° constraint is not derived or validated in this paper; it is imported from ref. 34, a prior article by the same research group, with overlapping authors, that also supplied the deconstruction code and cutoffs. The phrase 'inherited from a previous work' marks this as a citation-loaded ansatz for edge validity. Immediately afterward the paper falsifies the ansatz as a validity criterion by noting that sulfonyldibenzene in CoRE has a connecting angle below 140° yet forms valid MOFs. The load-bearing definition of 'geometric validity' therefore rests on a self-citation that is itself a heuristic rather than an external, verified condition.

full rationale

The reported validity statistics are partly circular. Section 4b curates training building blocks using a 0.3 Å node RMSD cutoff and a 140° edge-angle cutoff; Section 2b scores generated MOFs as valid by applying the same geometric criteria (node RMSD on net, edge connecting angle 140–180°). Thus the headline 52% overall validity, 63% edge validity, and 82% node validity largely measure how often the generated structures pass the very filters used to define the training distribution. The paper itself admits the edge-angle rule is not a true validity condition, citing sulfonyldibenzene in CoRE with angles below 140° that form valid MOFs; this makes the 'great geometric validity' claim in the abstract overstate what the metric establishes. The 140° rule is also imported from prior work by the same authors (ref. 34) without independent validation, so the validity definition is partly an ansatz smuggled in via self-citation. The circularity is partial, not total. The model's scalability to ~1000-atom cells, its novelty/uniqueness statistics relative to CoRE, the RAC-distribution comparison against the independent ToBaCCo database, and one experimental synthesis provide content independent of the curation filters. The synthesized MOF also differs from the model's predicted solvent coordination, so the experimental anchor is qualified. Because the abstract's central 'great geometric validity' claim rests substantially on a validity metric that reduces by construction to the training-set filters, but the paper retains independent evidence, the score is 6 rather than higher.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests mainly on a curated training set and hand-chosen geometric cutoffs inherited from prior work; no new physical entities are postulated. The model's novelty comes from the generative architecture rather than from new physical constraints.

free parameters (3)
  • edge angle cutoff = 140 degrees
    Inherited from prior work (ref 34); defines which generated edges count as valid and shapes the training set, yet the paper admits sulfonyldibenzene in CoRE has angle below this cutoff and is a valid MOF.
  • node RMSD cutoff = 0.3 Å
    Inherited from prior work (ref 34, 83); used to assign nodes to topological nets in training and to judge validity of generated nodes.
  • four topological nets = dia, nbo, sra, pcu
    Manual restriction on the set of nets used for generation, motivated by prevalence in experimental MOFs but limiting the claimed expansion of chemical space.
assumptions (4)
  • domain assumption SE(3)-equivariant diffusion with LEFTNet backbone correctly models the joint distribution of building blocks in CoRE MOF 2019.
    The model relies on the expressiveness of the object-aware SE(3) framework from OA-ReactDiff applied to building blocks; no formal guarantee is given for MOF chemistry.
  • domain assumption The automated deconstruction algorithm correctly separates MOFs into inorganic nodes, organic edges, and nets.
    Section 4b: the adjacency interpretation uses pairwise distance cutoffs; errors here propagate into the training distribution and validity definitions.
  • domain assumption Geometric validity of a generated MOF can be judged by node RMSD below 0.3 Å on the net and edge angle between 140 and 180 degrees.
    Section 2b and SI Figures 1-2; the paper itself notes this cutoff excludes valid MOFs, so the validity criterion is not an independent measure of synthesizability.
  • domain assumption Chemical compositions are provided as inputs; generating only atomic coordinates is sufficient for useful MOF design.
    Section 2e and 4a: the model keeps atom types fixed and samples only coordinates; this assumes the user knows the composition, which the paper acknowledges in the Conclusions.

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Cite this review

Pith. "Pith review of Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks." pith.science (2026). https://pith.science/paper/PD5BAVIR

@misc{pith2026250508531,
  author       = {Pith},
  title        = {Pith review of: Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PD5BAVIR}},
  note         = {Machine review of arXiv:2505.08531}
}
read the original abstract

Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force synthesis. Generative modeling holds ultimate promise, but existing models either recycle known building blocks or are restricted to small unit cells. We introduce Building-Block-Aware MOF Diffusion (BBA MOF Diffusion), an SE(3)-equivariant diffusion model that learns 3D all-atom representations of individual building blocks, encoding crystallographic topological nets explicitly. Trained on the CoRE-MOF database, BBA MOF Diffusion readily samples MOFs with unit cells containing 1000 atoms with great geometric validity, novelty, and diversity mirroring experimental databases. Its native building-block representation produces unprecedented metal nodes and organic edges, expanding accessible chemical space by orders of magnitude. One high-scoring [Zn(1,4-TDC)(EtOH)2] MOF predicted by the model was synthesized, where powder X-ray diffraction, thermogravimetric analysis, and N2 sorption confirm its structural fidelity. BBA-Diff thus furnishes a practical pathway to synthesizable and high-performing MOFs.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.