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SUMMARY:A Graph-Neural Network-Based model for rapid prediction of Thermal
  Transport in Metal-Organic Frameworks - Meiirbek Islamov
DTSTART;TZID=America/Chicago:20230714T135500
DTEND;TZID=America/Chicago:20230714T142500
DTSTAMP:20261015T193656Z
UID:pretalx-2023-EYHNUV@cfp.scipy.org
DESCRIPTION:Metal-Organic Frameworks (MOFs) have vast potential for gas ad
 sorption\, but their practical use hinges on their ability to dissipate th
 ermal energy generated during adsorption. Here\, we performed the first hi
 gh-throughput screening of thermal conductivity in over 10\,000 MOFs using
  molecular dynamics simulations. Next\, we developed a graph neural networ
 k (GNN) based model to swiftly predict the diagonal components of the ther
 mal conductivity tensor for accelerated materials discovery. Attendees wil
 l gain insights into how GNNs can be trained to predict material tensor pr
 operties\, benefiting both the materials science and machine learning comm
 unities.
LOCATION:Grand Salon C
URL:https://cfp.scipy.org/2023/talk/EYHNUV/
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