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Abstract Metal‐organic frameworks (MOFs) are porous, crystalline materials constructed from organic linkers and inorganic nodes with myriad potential applications in chemical separations, catalysis, and drug delivery. A major barrier to the application of MOFs is their poor scalability, as most frameworks are prepared under highly dilute solvothermal conditions using toxic organic solvents. Herein, we demonstrate that combining a range of linkers with low‐melting metal halide (hydrate) salts leads directly to high‐quality MOFs without added solvent. Frameworks prepared under these ionothermal conditions possess porosities comparable to those prepared under traditional solvothermal conditions. In addition, we report the ionothermal syntheses of two frameworks that cannot be prepared directly under solvothermal conditions. Overall, the user‐friendly method reported herein should be broadly applicable to the discovery and synthesis of stable metal‐organic materials.
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This content will become publicly available on May 1, 2026
Genetic algorithm-optimized machine learning models for drug loading prediction
Metal-organic frameworks (MOFs), made from metal ions and organic linkers, are promising materials for drug delivery due to their porous morphology. These components significantly affect drug loading, but the wide variety of irons and linkers makes it challenging to systematically evaluate their drug loading capacities. Machine Learning (ML) provides predictive models for drug loading based on properties such as ion type, linker structure, and MOFs morphology (e.g. surface area). However, the accuracy of these models is affected by hyperparameters. To improve model performance, this work develops a genetic algorithm (GA)-based optimization approach to build ML models for predicting drug loading rates. Our results demonstrate the predictability and generalizability of this approach for estimating the drug-loading capacities of different material-drug combinations.
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- PAR ID:
- 10620806
- Publisher / Repository:
- World Scientific
- Date Published:
- Journal Name:
- Functional Materials Letters
- Volume:
- 18
- Issue:
- 04
- ISSN:
- 1793-6047
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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