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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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Boosting predictive accuracy of single particle models for lithium-ion batteries using machine learning
The accuracy of single particle (SP) models for lithium-ion batteries at high C-rates is constrained by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates. This study addresses these limitations using extreme gradient boosting machine learning (ML). By training our ML model with data from a comprehensive electrochemical (P2D) model and performing sensitivity analysis on key battery parameters, we enhance predictive accuracy. Compared to conventional SP and P2D models under constant current loading, our ML-based SP model achieves similar predictive accuracy to P2D, with significant improvements in computational efficiency. Additionally, the ML-based SP model demonstrates improved predictive accuracy under dynamic loading conditions, providing a practical framework for improving battery management and safety.
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- Award ID(s):
- 2028992
- PAR ID:
- 10560574
- Publisher / Repository:
- AIP Publishing
- Date Published:
- Journal Name:
- Applied Physics Letters
- Volume:
- 125
- Issue:
- 14
- ISSN:
- 0003-6951
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0230376
