We successfully utilized OCLF ORNL GPU computing resources for efficient uncertainty analysis, which addressed the computational overhead caused by our proposed probabilistic models.
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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
Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge, considering that they can generate millions of lines of log data in a very short time.
Large-scale numerical calculations reveal fluctuating spin and charge stripes intertwined with pair-density-wave
Quantum Monte Carlo (QMC) methods are used to find the structure and electronic band gap of 2D GeSe, determining that the gap and its nature are highly tunable by strain.