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Award ID contains: 2143268

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  1. ; ; (, MRS Communications)
    Free, publicly-accessible full text available June 16, 2026
  2. ; ; (, Functional Materials Letters)
    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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    Free, publicly-accessible full text available May 1, 2026
  3. ; (, Microporous and Mesoporous Materials)
    Free, publicly-accessible full text available May 1, 2026
  4. ; (, Materials Letters)
    Free, publicly-accessible full text available February 1, 2026
  5. ; (, Chemical Physics Letters)
    Free, publicly-accessible full text available February 1, 2026
  6. ; ; (, Materials Today Communications)
    Full Text Available