A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
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Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
A numerical weather forecasting model (WRF) was used to simulate 120 storms over the Alabama-Coosa-Tallapoosa (ACT) river basin to explore the effect of climate change on probable maximum precipitation (PMP).