ZeoAgent: Autonomous Design of Zeolite Frameworks through Pore-Topology-Guided Generation and Evaluation

For zeolites, an important class of crystalline porous materials, this paradigm could address a long-standing challenge in framework discovery: the lack of rational strategies for coordinating framework generation with property objectives. Yet building such an autonomous agent system for zeolites remains challenging, as performance is governed by periodic pore topology and strict framework connectivity constraints.

This requires the agent to jointly reason over pore architectures and generate valid frameworks. Here we present ZeoAgent, a zeolite-specific system that converts natural-language objectives into computational operations for zeolite structure analysis and property prediction as well as framework design. ZeoAgent uses a point-cloud representation of pore architecture to learn from existing zeolite frameworks and guide the generation of framework candidates, which are then evaluated against the design objective within the same loop.