Mofasa: A Step Change in Metal-Organic Framework Generation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908684153520128 |
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| author | Simkus, Vaidotas Christensen, Anders Bennett, Steven Johnson, Ian Neumann, Mark Gin, James Godwin, Jonathan Rhodes, Benjamin |
| author_facet | Simkus, Vaidotas Christensen, Anders Bennett, Steven Johnson, Ian Neumann, Mark Gin, James Godwin, Jonathan Rhodes, Benjamin |
| contents | Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry.
In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies.
To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mofasa: A Step Change in Metal-Organic Framework Generation Simkus, Vaidotas Christensen, Anders Bennett, Steven Johnson, Ian Neumann, Mark Gin, James Godwin, Jonathan Rhodes, Benjamin Machine Learning Materials Science Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry. In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies. To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ . |
| title | Mofasa: A Step Change in Metal-Organic Framework Generation |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2512.01756 |