Machine learning with light scattering data to design complex copolymers
Machine learning and AI models are positioned to revolutionize materials chemistry by deconstructing complex structure-function relationships. However, large amounts of data are required to sufficiently train these models. In this webinar, we will show how data from high-throughput dynamic light scattering (HT-DLS) instruments utilizing microwell plates can inform these complex models. In an example, we will show how these models can be used to design complex synthetic copolymers.
Key Learning Objectives:
- How HT-DLS data can be collected in high-throughput in combination with laboratory robotics
- How HT-DLS data can be used to train machine learning models
- How high-throughput copolymer design studies are carried out with these techniques
Who Should Attend:
- Material scientists and polymer chemists
- Data scientists in the chemical and materials industry
- Chemical engineers working with soft matter in high throughput
Brought to you by:
Assistant Professor & Biomedical Engineering,
Rutgers University
National Field Application Scientist Team Leader,
Wyatt Technology Corporation
Contributing Editor,
C&EN Media Group
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Start:
Thu, Mar 24, 2022 11:00 AM EDT
End:
Thu, Mar 24, 2022 12:00 PM EDT
