Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
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arXiv
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| Format: | Preprint |
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2025
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| author | Duan, Chenru Nandy, Aditya Liu, Sizhan Du, Yuanqi He, Liu Qu, Yi Jia, Haojun Dou, Jin-Hu |
| author_facet | Duan, Chenru Nandy, Aditya Liu, Sizhan Du, Yuanqi He, Liu Qu, Yi Jia, Haojun Dou, Jin-Hu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks Duan, Chenru Nandy, Aditya Liu, Sizhan Du, Yuanqi He, Liu Qu, Yi Jia, Haojun Dou, Jin-Hu Chemical Physics Materials Science Machine Learning 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. |
| title | Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks |
| topic | Chemical Physics Materials Science Machine Learning |
| url | https://arxiv.org/abs/2505.08531 |