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Quantum Monte Carlo simulations reveal that Cooper pairs in the cuprate high-Tc superconductors are composed of electron holes on the Cu-d orbital and on the bonding molecular orbital constructed from the four surrounding O-p orbitals.
Generative machine learning models, including GANs (Generative Adversarial Networks), are a powerful tool toward searching chemical space for desired functionalities.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
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