Description
Metal-Organic Frameworks (MOFs) have vast potential for gas adsorption, but their practical use hinges on their ability to dissipate thermal energy generated during adsorption. Here, we performed the first high-throughput screening of thermal conductivity in over 10,000 MOFs using molecular dynamics simulations. Next, we developed a graph neural network (GNN) based model to swiftly predict the diagonal components of the thermal conductivity tensor for accelerated materials discovery. Attendees will gain insights into how GNNs can be trained to predict material tensor properties, benefiting both the materials science and machine learning communities.