A chemical language model for reticular materials design

2026-03-20

AI Summary

This work proposes Nexerra-R1, a novel framework that introduces a controllable chemical language model into reticular chemistry to overcome the inefficiencies of trial-and-error approaches in traditional metal–organic framework (MOF) design. By operating at the level of molecular building units, Nexerra-R1 enables inverse generation of organic linkers tailored to target properties. The model employs flow-guided distribution optimization and incorporates symmetry-aware, multidentate linker designs to support both unconstrained and topology-constrained modular synthesis. Integrated with three-dimensional structure assembly, it generates synthetically accessible MOF candidates. The approach successfully reproduces known MOFs and fully computationally designs a new, experimentally realizable framework, CU-525, thereby establishing a new inverse design paradigm that bridges performance objectives with experimental synthesis.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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-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.

Problem

• reticular chemistry
• metal-organic frameworks
• chemical space
• inverse design
• linker design

Innovation

• chemical language model
• inverse design
• reticular materials
• organic linkers
• flow-guided generation