project-39-localGPS_for_COF.md

number	title	topic	team_leads	contributors	github	youtube_video
39	Divide and Conquer - Local Gaussian Processes to design Covalent Organic Frameworks for Methane Deliverable Capacity	real-world	Nikhil Thota (Johns Hopkins University) @T-NIKHIL	Maitreyee Sharma Priyadarshini (Johns Hopkins University) @msharmap Yiran (Gigi) Wang (Johns Hopkins University) @gigiwang08 Jarett Ren (Johns Hopkins University) @jren0	AC-BO-Hackathon/project-localGPs_for_COF	iog-07Ekp9g

In this project, we will explore the application and performance of local GP models in the Bayesian Optimization framework to maximize Methane Deliverable Capacity of COFs. The methane deliverable capacity is important as it amounts to the amount of natural gas that can be stored on board vehicles. We use a database of 70,000 hypothetical COFS for testing this method [1].

Check out our submission post on X!

References:

1. Deshwal, A.; Simon, C. M.; Doppa, J. R. Bayesian Optimization of Nanoporous Materials. Mol. Syst. Des. Eng. 2021, 6 (12), 1066–1086. https://doi.org/10.1039/D1ME00093D.