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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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.
Researchers built a deep neural network to estimate the compressibility of scientific data.
A new method was developed for the discovery of fundamental descriptors for gas adsorption through deep learning neural network (DNN) approach. This approach has great potential to identify structural parameters for gas adsorption.
The upcoming Square Kilometre Array (SKA) will be the largest radio telescope in the world. An international team recently used Summit, the world’s most powerful supercomputer, to simulate the massive amounts of data the SKA will produce.
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
Direct experimental evidence of gas-phase methyl radicals in propane oxidative dehydrogenation (ODHP) combined with density functional theory (DFT) calculations uncovers the mechanism behind the exceptional selectivity to olefins over BN catalysts