About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Data-Driven Modelling of Graphene Synthesis |
Author(s) |
Aagam Shah, Mitisha Surana, Jad Yaacoub, Elif Ertekin, Sameh Tawfick |
On-Site Speaker (Planned) |
Aagam Shah |
Abstract Scope |
Despite that graphene synthesis via chemical vapor deposition (CVD) was first demonstrated in 2009, today economically viable manufacturing of continuous single crystals of graphene is still elusive. Hundreds of coupled synthesis parameters and complex kinetic pathways impede the development of recipes that are reproducible, environmentally sustainable, and compatible with electronics fabrication. To overcome these challenges, we present Gr-ResQ (Graphene Recipes for Synthesis of High-Quality Materials) - a platform enabling the sharing and use of graphene synthesis data. At its core is a crowd-sourced database of CVD recipes and characterization with a suite of tools to enable quick analysis of Raman spectra and microscopy images. We then propose a Bayesian optimization technique that uses the experimental results in the database to automate the search for an optimal synthesis recipe. This technique can guide the selection of the next experiment and iterate through the process till a predictive model is prepared. |
Proceedings Inclusion? |
Undecided |