[2603.20389] A chemical language model for reticular materials design

[2603.20389] A chemical language model for reticular materials design

arXiv - Machine Learning 4 min read

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Abstract page for arXiv paper 2603.20389: A chemical language model for reticular materials design

Condensed Matter > Materials Science arXiv:2603.20389 (cond-mat) [Submitted on 20 Mar 2026] Title:A chemical language model for reticular materials design Authors:Dhruv Menon, Vivek Singh, Xu Chen, Mohammad Reza Alizadeh Kiapi, Ivan Zyuzin, Hamish W. Macleod, Nakul Rampal, William Shepard, Omar M. Yaghi, David Fairen-Jimenez View a PDF of the paper titled A chemical language model for reticular materials design, by Dhruv Menon and 9 other authors View PDF HTML (experimental) Abstract: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...

Originally published on March 24, 2026. Curated by AI News.

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