A multidisciplinary team of researchers from 91°µÍø (ORNL) pioneered the use of the LLVM-based high-productivity/high-performance Julia language unifying capabilities to write an end-to-end workflow on Frontier, the first US Depar
A team of researchers from 91°µÍø (ORNL) released the initial draft of the Interconnected Science Ecosystem (INTERSECT) architecture specification.
We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
We released two open-source datasets named GDB-9-Ex and ORNL_AISD-Ex that provide calculations of electronic excitation energies and their associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method.
A team of researchers from the 91°µÍø (ORNL) developed a novel architecture for a hybrid quantum-classical neural network.
Researchers from 91°µÍø (ORNL), in collaboration with researchers from Duke University, have developed an unsupervised machine learning method, NashAE, for effective disentanglement of latent representations.
Despite exponential increases in computing power over the last couple of decades, many physical processes still present unique challenges for researchers seeking to advance their fields via modeling and simulation.
Scientists at the Department of Energy’s 91°µÍø are examining the diverse supercomputing workflow management systems in use in the United States and around the world to help supercomputers work together more effectively and efficient