Machine learning with light scattering data to design complex copolymers

March 24, 2022
8:00 a.m. PDT, 11:00 a.m. EDT, 15:00 GMT, 16:00 CET - Duration: 60 Minutes


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:
Wyatt Technology


Dr. Adam Gormley
Assistant Professor & Biomedical Engineering,
Rutgers University
Eric Seymour
National Field Application Scientist Team Leader,
Wyatt Technology Corporation
Jeff Huber
Contributing Editor,
C&EN Media Group


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