Close

Presentation

Carbon Catcher
DescriptionUsing GFlowNets, we generate porous reticular materials, such as Metal-Organic Frameworks and Covalent Organic Frameworks, for applications in carbon dioxide capture. We introduce a new Python package (matgfn) to train and sample GFlowNets. We use matgfn to generate the matgfn-rm dataset of novel and diverse reticular materials with gravimetric surface area above 5000 m2 g−1. We calculate single- and two-component gas adsorption isotherms for the top 100 candidates in matgfn-rm. These candidates are novel compared to the state-of-the-art ARC-MOF dataset and rank in the 90th percentile in terms of working capacity compared to the CoRE2019 dataset. We identify 13 materials with CO2 working capacity outperforming all materials in CoRE2019. After further analysis and structural relaxation, two outperforming materials remain (https://pubs.rsc.org/en/content/articlelanding/2024/dd/d4dd00020j).

Once the .xyz files were created, they were then handed over to the visualisation team, where they were imported into VMD. Once in VMD, the Van Der Waals graphical representation was chosen and then imported into Blender. In Blender, the lighting, materials, textures and environment were all altered to show the MOFs in an artistic way.
Event Type
Art of HPC
Posters
TimeTuesday, 19 November 202410am - 5pm EST
LocationB301
Registration Categories
TP
W
TUT
XO/EX