MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908273895014400 |
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| author | Kim, Nayoung Kim, Seongsu Kim, Minsu Park, Jinkyoo Ahn, Sungsoo |
| author_facet | Kim, Nayoung Kim, Seongsu Kim, Minsu Park, Jinkyoo Ahn, Sungsoo |
| contents | Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure prediction. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. To address this limitation, we propose a novel Riemannian flow matching framework that reduces the dimensionality of the problem by treating the metal nodes and organic linkers as rigid bodies, capitalizing on the inherent modularity of MOFs. By operating in the $SE(3)$ space, MOFFlow effectively captures the roto-translational dynamics of these rigid components in a scalable way. Our experiment demonstrates that MOFFlow accurately predicts MOF structures containing several hundred atoms, significantly outperforming conventional methods and state-of-the-art machine learning baselines while being much faster. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks Kim, Nayoung Kim, Seongsu Kim, Minsu Park, Jinkyoo Ahn, Sungsoo Biomolecules Materials Science Machine Learning Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure prediction. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. To address this limitation, we propose a novel Riemannian flow matching framework that reduces the dimensionality of the problem by treating the metal nodes and organic linkers as rigid bodies, capitalizing on the inherent modularity of MOFs. By operating in the $SE(3)$ space, MOFFlow effectively captures the roto-translational dynamics of these rigid components in a scalable way. Our experiment demonstrates that MOFFlow accurately predicts MOF structures containing several hundred atoms, significantly outperforming conventional methods and state-of-the-art machine learning baselines while being much faster. |
| title | MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks |
| topic | Biomolecules Materials Science Machine Learning |
| url | https://arxiv.org/abs/2410.17270 |