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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- (-) Computational Chemistry (3)
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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
Efforts to bring ORNL’s wireless sensor platform to market are on target and proceeding as planned.
Achievement: Designed a polymer for selective and reversible carbon dioxide (CO2) capture.
Significance and Impact: The new polymer that is based on amidines can provide a more efficient alternative to conventional polyethyleneimine (PEI) based solid-sorb
A molecule, called a nucleoside analog and which is composed of an Adenine moeity and glycol group, was deposited on top of the Au(111) surface and studied with scanning tunneling microscopy and density functional theory calculations.
In this work unique twisted bilayers of MoSe2 with periodic multiple stacking configurations and interlayer couplings were discovered in the narrow range of twist angles, 60± 3°, using ulra-low frequency Raman spectroscopy and first-principle theory.