Connecting metal-organic framework synthesis to applications using multimodal machine learning
Abstract Every year, researchers create hundreds of thousands of new materials, each with unique structures and properties. For example, over 5000 new metal-organic frameworks (MOFs) were reported in the past year alone. While these materials are often synthesized for specific applications, they may...
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60796-0 |
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| author | Sartaaj Takrim Khan Seyed Mohamad Moosavi |
| author_facet | Sartaaj Takrim Khan Seyed Mohamad Moosavi |
| author_sort | Sartaaj Takrim Khan |
| collection | DOAJ |
| description | Abstract Every year, researchers create hundreds of thousands of new materials, each with unique structures and properties. For example, over 5000 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, linking these new materials to their best applications 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, even when small data is available. 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. We provide this tool as an open source code and a web app to accelerate the matching of new materials with their potential industrial applications. |
| format | Article |
| id | doaj-art-de80e1838c8d4a6091de8dbbecbb1cd3 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-de80e1838c8d4a6091de8dbbecbb1cd32025-08-20T03:03:41ZengNature PortfolioNature Communications2041-17232025-07-0116111010.1038/s41467-025-60796-0Connecting metal-organic framework synthesis to applications using multimodal machine learningSartaaj Takrim Khan0Seyed Mohamad Moosavi1Chemical Engineering & Applied Chemistry, University of TorontoChemical Engineering & Applied Chemistry, University of TorontoAbstract Every year, researchers create hundreds of thousands of new materials, each with unique structures and properties. For example, over 5000 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, linking these new materials to their best applications 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, even when small data is available. 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. We provide this tool as an open source code and a web app to accelerate the matching of new materials with their potential industrial applications.https://doi.org/10.1038/s41467-025-60796-0 |
| spellingShingle | Sartaaj Takrim Khan Seyed Mohamad Moosavi Connecting metal-organic framework synthesis to applications using multimodal machine learning Nature Communications |
| title | Connecting metal-organic framework synthesis to applications using multimodal machine learning |
| title_full | Connecting metal-organic framework synthesis to applications using multimodal machine learning |
| title_fullStr | Connecting metal-organic framework synthesis to applications using multimodal machine learning |
| title_full_unstemmed | Connecting metal-organic framework synthesis to applications using multimodal machine learning |
| title_short | Connecting metal-organic framework synthesis to applications using multimodal machine learning |
| title_sort | connecting metal organic framework synthesis to applications using multimodal machine learning |
| url | https://doi.org/10.1038/s41467-025-60796-0 |
| work_keys_str_mv | AT sartaajtakrimkhan connectingmetalorganicframeworksynthesistoapplicationsusingmultimodalmachinelearning AT seyedmohamadmoosavi connectingmetalorganicframeworksynthesistoapplicationsusingmultimodalmachinelearning |