Machine learning with light scattering data to design complex copolymers

Published on ‎03-09-2022 11:35 AM by

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


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%7B805cfe2b-23dc-4cf0-aacb-474b8fc8383d%7D_2022-3-24-Adam-Gormley-speakerDr. Adam Gormley
Assistant Professor & Biomedical Engineering,
Rutgers University

%7B29f96068-1d70-41d0-9197-492d3b236ed8%7D_2022-3-24-Eric-Seymour-speakerEric Seymour
National Field Application Scientist Team Leader,
Wyatt Technology Corporation

%7B21b598ba-2530-4df4-a05c-d4f038f750ef%7D_jeffhuber_100x100Jeff Huber
Contributing Editor,
C&EN Media Group

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Thu, Mar 24, 2022 11:00 AM EDT
Thu, Mar 24, 2022 12:00 PM EDT
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