A comprehensive mapping of zeolite-template chemical space

Zeolites are industrially important catalysts and adsorbents, typically synthesized using specific molecules known as organic structure-directing agents (OSDAs). The templating effect of the OSDAs is pivotal in determining the zeolite polymorph formed and its physicochemical properties. However, de novo design of selective OSDAs is challenging because of the diversity and size of the zeolite-OSDA chemical space. Here we present ZeoBind, a computational workflow powered by machine learning that enables an exhaustive exploration of the OSDA space. We design predictive tasks that capture zeolite-molecule matching, train predictive models for these tasks on hundreds of thousands of datapoints and curate a library of 2.3 million synthetically accessible, hypothetical OSDA-like molecules enumerated from commercially available precursors. We use ZeoBind to screen nearly 500 million zeolite-molecule pairs and identified and experimentally validated two new OSDAs that template zeolites with novel compositions. The scale of the OSDA library, along with the open-access tools and data, has the potential to accelerate OSDA design for zeolite synthesis.