mlsanityData to 14 Oct 2026

MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design

arXiv 2310.10732 ↗First posted 16 Oct 2023physics.chem-ph cond-mat.mtrl-sci cs.LGcited by 24

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Abstract (arXiv)

Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modular nature has enabled the use of template-based methods to generate hypothetical MOFs by combining molecular building blocks in accordance with known network topologies. However, the ability of these methods to identify top-performing MOFs is often hindered by the limited diversity of the resulting chemical space. In this work, we propose MOFDiff: a coarse-grained (CG) diffusion model that generates CG MOF structures through a denoising diffusion process over the coordinates and identities of the building blocks. The all-atom MOF structure is then determined through a novel assembly algorithm. Equivariant graph neural networks are used for the diffusion model to respect the permutational and roto-translational symmetries. We comprehensively evaluate our model's capability to generate valid and novel MOF structures and its effectiveness in designing outstanding MOF materials for carbon capture applications with molecular simulations.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 34 of its 98 references that are in the map. First posted on arXiv 16 Oct 2023.

Builds on

34 of 98 references.

References · 34

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22 more
  1. 2020Directional Message Passing for Molecular Graphscited by 561
  2. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
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  19. 2021MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworkscited by 11
  20. 2023Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blockscited by 8
  21. 2022A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Modelscited by 7
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 12; 2026: 9.

24 papers.

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  1. 2025All-atom Diffusion Transformers: Unified generative modelling of molecules and materialscited by 75
  2. 2025Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systemscited by 22
  3. 2024MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworkscited by 17
  4. 2025Riemannian Variational Flow Matching for Material and Protein Designcited by 11
  5. 2025MOFGPT: Generative Design of Metal-Organic Frameworks using Language Modelscited by 11
  6. 2024AI-driven inverse design of materials: Past, present and futurecited by 10
  7. 2025Materials Generation in the Era of Artificial Intelligence: A Comprehensive Surveycited by 10
  8. 2025Flexible MOF Generation with Torsion-Aware Flow Matchingcited by 10
  9. 2024AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusioncited by 8
  10. 2025The Rise of Generative AI for Metal-Organic Framework Design and Synthesiscited by 6
  11. 2025Mofasa: A Step Change in Metal-Organic Framework Generationcited by 5
  12. 2025Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworkscited by 4
12 more
  1. 2025Generative Models for Crystalline Materialscited by 4
  2. 2026Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciencescited by 3
  3. 2025EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecturecited by 2
  4. 2026SLayerGen: a Crystal Generative Model for all Space and Layer Groupscited by 2
  5. 2025Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolitescited by 0
  6. 2026Inverse Design of Inorganic Compounds with Generative AIcited by 0
  7. 2026LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Designcited by 0
  8. 2026Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agentscited by 0
  9. 2026Large language model agents accelerate inverse design of metal-organic frameworks for gas separationcited by 0
  10. 2026GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generationcited by 0
  11. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0
  12. 2026AnchorPose for Geometry-Aware MOF Assembly through Meso-Grained Pose Generationcited by 0

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  2. 2026Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
  3. 2025System of Agentic AI for the Discovery of Metal-Organic Frameworks
  4. 2026An LLM agent for end-to-end computational materials discovery
  5. 2025Reshaping MOFs text mining with a dynamic multi-agents framework of large language model
  6. 2026SimMOF: AI agent for Automated MOF Simulations
  7. 2025L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks
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