A scientific machine learning framework to understand flash graphene synthesis
Abstract
The SML model was trained on both direct experimental and indirect physics-informed features to predict graphene quality synthesized from Flash Joule heating. With an R2 of 0.81, the model performs better compared to 0.73 without indirect features.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2023
- Source ID
- 10.1039/d3dd00055a
Entities
People
- Jacob L. Beckham
- James Tour
- Jian Lin
- Kevin M. Wyss
- Kianoosh Sattari
- Long Qian
- Lucas Eddy
- Richard Byfield
Organizations
- Air Force Office of Scientific Research
- Engineer Research and Development Center
- National Science Foundation
- Rice University
- University of Missouri