Connecting metal-organic framework synthesis to applications with a self-supervised multimodal model

Every year, researchers create hundreds of thousands of new materials, each with unique structures and properties. For example, over 5,000 new metal-organic frameworks (MOFs) were reported in the past year alone. While these materials are often synthesized for specific applications, they may have potential uses in entirely different domains. However, identifying the best applications for new materials remains a significant challenge. In this study, we demonstrate a multimodal approach that uses the information available as soon as a MOF is synthesized, specifically its powder X-ray diffraction pattern (PXRD) and the chemicals used in its synthesis, to predict its potential properties and uses. By self-supervised pretraining of this model on crystal structures accessible from MOF databases, our model achieves accurate predictions for various properties, across pore structure, chemistry-reliant, and quantum-chemical properties. We further assess the robustness of this method in the presence of experimental measurement imperfections. Utilizing this approach, we create a synthesis-to-application map for MOFs, offering insights into optimal material classes for diverse applications. Finally, by augmenting this model with a recommendation system, we identify promising MOFs for applications that are different from the originally reported applications. The open source code and the web app for this research enable interlaboratory collaboration, particularly when materials characterization infrastructure are not available in one group, thereby accelerating the matching of new materials with their industrial applications.