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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.
A team at ORNL has demonstrated that the combination of transfer learning and semi-supervised learning can significantly reduce the amount of labeled data required to obtain strong performance in biomedical named entity recognition (NER) tasks.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
A team of researchers from Oak Ridge National Laboratory (ORNL) designed, implemented, and evaluated a high-performance computing (HPC) runtime system.
Researchers from Oak Ridge National Laboratory and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.
A team of researchers from Oak Ridge National Laboratory applied advanced statistical methods from biomedical research to study an unexpected failure mode of general-purpose computing on graphics processing units (GPGPUs).
Researchers developed a novel algorithm for resilient and communication-efficient parallel matrix multiplication in HPC systems.