Title: Flexible MOF Generation with Torsion-Aware Flow Matching
Venue: NeurIPS 2025
Accepted: yes
Keywords: metal-organic frameworks

crystal generation

flow matching
Abstract: Designing metal-organic frameworks (MOFs) with novel chemistries is a longstanding challenge due to their large combinatorial space and complex 3D arrangements of the building blocks. While recent deep generative models have enabled scalable MOF generation, they assume (1) a fixed set of building blocks and (2) known local 3D coordinates of building blocks. However, this limits their ability to (1) design novel MOFs and (2) generate the structure using novel building blocks. We propose a two-stage MOF generation framework that overcomes these limitations by modeling both chemical and geometric degrees of freedom. First, we train an SMILES-based autoregressive model to generate metal and organic building blocks, paired with a cheminformatics toolkit for 3D structure initialization. Second, we introduce a flow matching model that predicts translations, rotations, and torsional angles to assemble the blocks into valid 3D frameworks. Our experiments demonstrate improved reconstruction accuracy, the generation of valid, novel, and unique MOFs, and the ability to create novel building blocks. Our code is available at https://github.com/nayoung10/MOFFlow-2.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MOFFLOW-2, a flow-based generative model specifically designed for flexible Metal-Organic Framework (MOF, a family of materials)  structure prediction and design problem. The first stage leverages a SMILES-based autoregressive Transformer to generate novel chemical building blocks, while the second employs a torsion-aware flow matching model to assemble these blocks into 3D structures. MOFFLOW-2 incorporates rotations, translations, torsion angles, and lattice parameters into structure modeling. Experiments demonstrate that MOFFLOW-2 surpasses previous state-of-the-art methods in structure prediction accuracy and successfully generates novel, valid, and unique MOFs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n- The introduction of torsion-aware flow matching significantly advances generative modeling capabilities for MOFs by addressing flexibility explicitly. \n- The paper provides extensive evaluations and comparisons with recent models, showcasing superior performance across critical metrics. \n- Demonstrates the ability to generate MOFs from novel building blocks, significantly extending the design space beyond existing methods (eg. MOFFlow-1)\n\nWeaknesses: \n- Although the method is effective, the computational cost and resource requirements for larger-scale and more complex MOF generation are insufficiently discussed (for example, the training cost, inference time, etc.), which could be relevant in real-world using scenario. It could be better to include these, more or less, in the main text.\n- The stability and real-world chemical feasibility of the generated (designed) MOFs, while briefly mentioned, lack comprehensive validation through computational chemistry methods (eg., DFT or ab initio calculations). That being said, it is okay to leave this for future work.\n- Given MOF is a specific family of molecules, there is limited exploration of how the method scales and performs with significantly different MOF types or more chemically diverse frameworks (more general family of molecules) as an ML for science work.\n\nQUESTIONS:\n1. The training of MOFFLOW-2 (as well as its baselines, MOFFLOW, MOFDIFF) seems to rely on a previous MOF dataset (Boyd et al.). Does the dataset itself enough for learning the distribution of MOF structure? In other words, do you think incorporating other data sources of large scale of molecule or material datasets for pretraining will help? If yes or no, why?\n2. In Table 2 of structure prediction task, under smaller tolerance threshold, why the MR and RMSE seems inconsistent by contrasting DiffCSP and MOFLOW-2? Can you explain this phenomenon?\n3. What is the major difference between MOFFlow-1 and -2? Which factor(s) cause the performance gain on the structure prediction task in Table 2?\n4. Regarding the design problem, how was the “valid, novel and unique” defined in Figure 5, table 3 and section 6.2? I could not find the definition of these metrics.\n5. By explicitly modeling the torsion angles, the modeled degree of freedom is augmented based on the model without it. Beyond that, what could be the potential “rigid” part in the MOF? In other words, which degrees of freedom are still not modeled under the setting of MOFFlow-2?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-24"}

comments: SUMMARY:
The paper introduced MOFFLOW-2, a two-stage generative framework for metal–organic frameworks that (1) generates metal clusters and organic linkers via an autoregressive SMILES model, and (2) assembles them into full 3D crystal structures by explicitly modelling translations, rotations, lattice, and torsional degrees of freedom using Riemannian flow matching.  The method has been evaluated on two tasks: structure prediction and generation.

STRENGTHS AND WEAKNESSES:
**Strengths**

- Two-stage framework: I think the idea of two-stage decomposition, first smiles generation, and then capturing 3D structure via flow matching is an effective solution for this task.
- Empirical Evaluation: The authors evaluated their method on two tasks and the method seems to has better RMSE in structure prediction compared to previous baselines. 

**Weaknesses**

- I think the evaluation is still limited, and the authors compared to classical methods and a few baselines like MOFDiff.
- The method still consider the use of bioinformatics tools like RDKit, as the second stage relies on an initial 3D structure for the organic linkers generated by RDKit. Also, while the model learns to correct torsion angles, it still assumes the initial bond lengths and angles are accurate.

QUESTIONS:
Regarding the initial 3D structure of linkers, did you investigate the model's sensitivity to the quality of the RDKit-generated conformer? Can you comment more on that?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MOFFLOW-2, a two-stage generative framework for metal–organic framework (MOF) design. The first stage leverages an autoregressive SMILES-based language model to generate novel metal and organic building blocks. The second stage employs a flow matching model to predict the 3D MOF structure by modeling translations, rotations, torsions, and lattice parameters. This approach addresses key limitations of prior work that relied on fixed, rigid building blocks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- Clear motivation and reasonable contribution: The paper addresses critical limitations of prior MOF generative models by enabling the generation of novel building blocks and incorporating torsional flexibility.\n- Substantial empirical improvements: On structure prediction, MOFFLOW-2 outperforms all tested baselines under strict matching criteria. On generative design, it shows higher validity, novelty, uniqueness, and property diversity.\n- Good writing and clarity: The paper is well-structured, with clear diagrams, descriptions, and motivation throughout.\n\n**Weaknesses:**\n- Lack of physical validation: The generated structures are not assessed using quantum mechanical (e.g., DFT) or molecular dynamics simulations, which limits confidence in their physical stability.\n- Limited task scope: The framework is not demonstrated in property-driven generation scenarios, which would be critical for targeted MOF design.\n\nQUESTIONS:\n- How sensitive is the performance of MOFFLOW-2 to the initial RDKit geometries used for organic linkers?\n- How well does the model generalize to MOF chemistries that are entirely out-of-distribution from training?\n\nLIMITATIONS:\n- The physical plausibility of the generated MOFs is not validated using quantum or force-field level simulations.\n- The framework does not show results in property-conditioned or goal-directed generation, which may limit its applicability in practical design tasks.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MOFFLOW-2, a two-stage generative framework designed for the prediction of metal-organic framework (MOF) structures and the creation of novel MOFs. The framework comprises two main components: (1) an autoregressive SMILES-based model for generating metal and organic building blocks, and (2) a flow-matching model for assembling these blocks into valid 3D structures by predicting translations, rotations, torsional angles, and lattice parameters. The approach addresses limitations of previous models by generating novel building blocks, predicting flexible 3D conformations, and not relying on a fixed set of building blocks or rigid conformations. Experimental results show that MOFFLOW-2 outperforms previous methods like MOFDiff and MOFFLOW in both structure prediction accuracy and the generation of novel, valid, and unique MOFs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ **Novel Approach**: The introduction of a two-stage framework that combines SMILES-based building block generation with flexible 3D structure prediction is innovative and allows for greater chemical diversity and structural flexibility.\n\n+ **Strong Experimental Results**: The model outperforms baseline methods in both structure prediction and MOF generation tasks, showing improved validity, novelty, and uniqueness in generated structures. This is particularly important for discovering new MOFs and expanding chemical space.\n\n+ **Clear Methodology and Comprehensive Architecture**: The authors present a clear and detailed explanation of their methodology, including model architecture, preprocessing steps, and the flow-matching process for predicting rotational, translational, and torsional transformations.\n\n\nWeaknesses:\n\n\n+ **Insufficient Ablation Studies**: A key concern is the absence of a clear breakdown of the contributions from individual model components (e.g., the SMILES generator and flow-matching) to overall performance. Since the generation process is factorized into two stages, it is crucial to independently analyze the contribution of each stage and identify potential bottlenecks. Is it possible to provide experiments that offer insight into this aspect?\n\nQUESTIONS:\nSee the weakeness.\n\nLIMITATIONS:\nYes, the authors have included a limitation section in their paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}