To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.
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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.
A team from Oak Ridge and Los Alamos National Laboratories led a demonstration of quantum key distribution systems that harness the power of quantum mechanics to authenticate data and encrypt messages with a secret ”key” to securely transmit “locked” in
ORNL researchers have developed a quantum chemistry simulation benchmark to evaluate the performance of quantum devices and guide the development of applications for future quantum computers.
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