Paper Number
AM7 Keynote
Session
AI and ML Based Rheological Characterization
Title
AI-enabled design of polymeric materials and their rheology from atomistic models
Presentation Date and Time
October 10, 2022 (Monday) 1:30
Track / Room
Track 6 / Mayfair
Authors
- de Pablo, Juan J. (The University of Chicago, Pritzker School of Molecular Engineering)
Author and Affiliation Lines
Juan J. de Pablo
Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637
Speaker / Presenter
de Pablo, Juan J.
Keywords
AI based; ML based
Text of Abstract
There is considerable interest in applying emerging concepts from machine learning and artificial intelligence to design new polymeric materials with superior performance. Some of the key challenges that must be addressed are to (1) combine multiscale models and computational methods with machine learning algorithms for property prediction (including rheology) and design, (2) generate hybrid data bases that incorporate computationally generated data and experimental data from multiple sources, (3) create workflows that enable automated generation of information and design of materials new systems. In this presentation I will provide an overview of the state-of-the-art in this general domain with examples taken from our own work and from the recent literature.