Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
- URL: http://arxiv.org/abs/2505.08531v1
- Date: Tue, 13 May 2025 13:02:28 GMT
- Title: Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
- Authors: Chenru Duan, Aditya Nandy, Sizhan Liu, Yuanqi Du, Liu He, Yi Qu, Haojun Jia, Jin-Hu Dou,
- Abstract summary: Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals.<n>Existing models either recycle known building blocks or are restricted to small unit cells.<n>We introduce Building-Block-Aware MOF Diffusion, an SE(3)-equivariant diffusion model that learns 3D all-atom representations of individual building blocks.
- Score: 10.094982948231923
- License: http://creativecommons.org/licenses/by/4.0/
- 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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