2022 Annual Meeting

Session: Data-Driven/Machine Learning-Enabled Design for Nanocomposites

Chair

Chen, P. Y., University of Maryland

Co-Chairs

Ike, S., Penn State University
Koh, A., University of Alabama

Presentations

08:00 AM

(697a) Machine Learning–Enabled Design of All-Natural Plastic Substitutes

08:20 AM

(697b) Accelerated Design of Flame Retardant Polymeric Nanocomposites Via Machine Learning

08:40 AM

(697c) Machine Learning Assisted Development of Electrochemical Cortisol Sensor Based on Electropolymerized Molecularly Imprinted Polymer

09:00 AM

(697d) Data-Driven Design of Mxene Aerogels with Programmable Mechanical Performance Via Active Learning and Collaborative Robots

09:20 AM

(697e) Monitoring Catalysts Synthesis Using Real-Time Emission Spectroscopy and Advanced Machine Learning Models

09:40 AM

(697f) Automatic Stretchable Conductor Design and Fabrication Via Machine Learning