Divide and Conquer - Local Gaussian Processes to design Covalent Organic Frameworks for Methane Deliverable Capacity | Bayesian Optimization Hackathon for Chemistry and Materials

Project 39:
Divide and Conquer - Local Gaussian Processes to design Covalent Organic Frameworks for Methane Deliverable Capacity

Project leads

Nikhil Thota (Johns Hopkins University) @T-NIKHIL

Contributors

Maitreyee Sharma Priyadarshini (Johns Hopkins University) @msharmap
Yiran (Gigi) Wang (Johns Hopkins University) @gigiwang08
Jarett Ren (Johns Hopkins University) @jren0

GitHub repository

github.com/AC-BO-Hackathon/project-localGPs_for_COF

Divide and Conquer - Local Gaussian Processes to design Covalent Organic Frameworks for Methane Deliverable Capacity

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:

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.