Machine learning with light scattering data to design complex copolymers

DATE

March 24, 2022

TIME

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

Overview

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:

Speakers

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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