Designing New Polymers for Human Health Applications
Case Study 08
Machine learning models trained on the client's polymer data guided the design and optimization of new polymers, with later measurements confirming the predictions.
Challenge
The project used client-provided polymer data and measured properties, including tensile strength, to guide the design of new polymers for human health applications.
What We Did
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1
Built property-prediction models
Used the client's polymer data to train support vector regression (SVR) and random forest regression (RFR) models for the measured properties.
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2
Validated model performance
Evaluated the models using data scrambling and 5-fold cross-validation.
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3
Designed and optimized polymers
Used predictions for monomers and the property models to guide the design and optimization of novel polymers.
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4
Worked in a rapid feedback cycle
Completed the project in three weeks with weekly updates and reports. The client later synthesized the polymers and measured polymer properties, confirming the predictions.