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.
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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
Dendritic solidification and microstructure evolution play a vital role in determining the material properties. Capturing the morphology of the solidification front becomes critical in predicting the final dendritic structure.
Efforts to bring ORNL’s wireless sensor platform to market are on target and proceeding as planned.