Flow-Guided Chemical Language Modeling for Linker Design in Reticular Chemistry

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 NexerraR1, a building-block chemical language model that enables inverse design in reticular chemistry through targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra 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 structurally optimized to produce candidate materials compatible with experimental synthesis. Using NexerraR1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated in silico. Together, these results establish a controllable building-block-level design framework for reticular chemistry in which chemical language modeling enables the direct translation from computational design to synthesizable frameworks.