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UID:submissions.supercomputing.org_SC24_sess554_art130@linklings.com
SUMMARY:Carbon Catcher
DESCRIPTION:Kelly Hanifin (Hartree Centre, Science and Technology Faciliti
 es Council (STFC), UK)\n\nUsing GFlowNets, we generate porous reticular ma
 terials, such as Metal-Organic Frameworks and Covalent Organic Frameworks,
  for applications in carbon dioxide capture. We introduce a new Python pac
 kage (matgfn) to train and sample GFlowNets. We use matgfn to generate the
  matgfn-rm dataset of novel and diverse reticular materials with gravimetr
 ic 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 ca
 ndidates are novel compared to the state-of-the-art ARC-MOF dataset and ra
 nk in the 90th percentile in terms of working capacity compared to the CoR
 E2019 dataset. We identify 13 materials with CO2 working capacity outperfo
 rming all materials in CoRE2019. After further analysis and structural rel
 axation, two outperforming materials remain (https://pubs.rsc.org/en/conte
 nt/articlelanding/2024/dd/d4dd00020j).\n\nOnce the .xyz files were created
 , they were then handed over to the visualisation team, where they were im
 ported into VMD. Once in VMD, the Van Der Waals graphical representation w
 as chosen and then imported into Blender. In Blender, the lighting, materi
 als, textures and environment were all altered to show the MOFs in an arti
 stic way.\n\nRegistration Category: Tech Program Reg Pass, Workshop Reg Pa
 ss, Tutorial Reg Pass, Exhibits Reg Pass\n\n
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