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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.20389 |
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| _version_ | 1866914412198100992 |
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| author | Menon, Dhruv Singh, Vivek Chen, Xu Kiapi, Mohammad Reza Alizadeh Zyuzin, Ivan Macleod, Hamish W. Rampal, Nakul Shepard, William Yaghi, Omar M. Fairen-Jimenez, David |
| author_facet | Menon, Dhruv Singh, Vivek Chen, Xu Kiapi, Mohammad Reza Alizadeh Zyuzin, Ivan Macleod, Hamish W. Rampal, Nakul Shepard, William Yaghi, Omar M. Fairen-Jimenez, David |
| contents | Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra-R1, a building-block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra-R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low-connectivity linkers and scaffold-constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow-guided distributional targeting to steer the generative process toward application-relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three-dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra-R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated entirely in silico. Together, these results establish a general inverse-design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20389 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A chemical language model for reticular materials design Menon, Dhruv Singh, Vivek Chen, Xu Kiapi, Mohammad Reza Alizadeh Zyuzin, Ivan Macleod, Hamish W. Rampal, Nakul Shepard, William Yaghi, Omar M. Fairen-Jimenez, David Materials Science Machine Learning Chemical Physics Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra-R1, a building-block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra-R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low-connectivity linkers and scaffold-constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow-guided distributional targeting to steer the generative process toward application-relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three-dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra-R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated entirely in silico. Together, these results establish a general inverse-design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks. |
| title | A chemical language model for reticular materials design |
| topic | Materials Science Machine Learning Chemical Physics |
| url | https://arxiv.org/abs/2603.20389 |