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MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
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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.
Forward citations
Cited by 5 Pith papers
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Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
A building-block-aware diffusion model generates novel, large-unit-cell MOF crystal structures, and one model-suggested MOF was synthesized with a structure close to, but not identical to, the prediction.
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System of Agentic AI for the Discovery of Metal-Organic Frameworks
An agentic AI pipeline generated hundreds of thousands of MOF candidates and produced five experimentally confirmed metal-organic frameworks, though only a few linkers are truly novel.
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MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
MOFA couples a fine-tuned diffusion model with LAMMPS, CP2K, and RASPA simulations in an online learning loop to generate stable MOFs with high CO2 adsorption, demonstrating near-linear scaling on up to 450 nodes.
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Machine Learning for Designing Undesignable Metal-Organic Frameworks
An ML pipeline combining reinforcement learning and graph neural networks claims to design MOF photocatalysts that outperform literature controls by over 125% in predicted fitness.
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AI-driven inverse design of materials: Past, present and future
A comprehensive survey of AI-driven inverse design of materials that summarizes existing methods and applications without presenting new results.
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