Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation
Abstract The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55390-9 |
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| _version_ | 1841559209509912576 |
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| author | Junkil Park Youhan Lee Jihan Kim |
| author_facet | Junkil Park Youhan Lee Jihan Kim |
| author_sort | Junkil Park |
| collection | DOAJ |
| description | Abstract The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) are employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibits exceptional generation performance, and demonstrates its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations. |
| format | Article |
| id | doaj-art-57c8fe6c9fb24dd1b5a53db1df2ebbf5 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-57c8fe6c9fb24dd1b5a53db1df2ebbf52025-01-05T12:39:51ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-024-55390-9Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generationJunkil Park0Youhan Lee1Jihan Kim2Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)NVIDIA CorporationDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)Abstract The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) are employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibits exceptional generation performance, and demonstrates its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations.https://doi.org/10.1038/s41467-024-55390-9 |
| spellingShingle | Junkil Park Youhan Lee Jihan Kim Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation Nature Communications |
| title | Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation |
| title_full | Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation |
| title_fullStr | Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation |
| title_full_unstemmed | Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation |
| title_short | Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation |
| title_sort | multi modal conditional diffusion model using signed distance functions for metal organic frameworks generation |
| url | https://doi.org/10.1038/s41467-024-55390-9 |
| work_keys_str_mv | AT junkilpark multimodalconditionaldiffusionmodelusingsigneddistancefunctionsformetalorganicframeworksgeneration AT youhanlee multimodalconditionaldiffusionmodelusingsigneddistancefunctionsformetalorganicframeworksgeneration AT jihankim multimodalconditionaldiffusionmodelusingsigneddistancefunctionsformetalorganicframeworksgeneration |