A team of researchers from Oak Ridge National Laboratory (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.
Simulations of red blood cells are important for a variety of biomedical applications, ranging from studies of blood diseases to the transport of circulating tumor cells.
A multidisciplinary team of researchers from Oak Ridge National Laboratory and the University of Texas at Austin developed a new machine-learning-based reduced-order model called GrainNN to predict the grain structure that forms as a metal solidifies.
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.
Researchers from Oak Ridge National Laboratory (ORNL), in collaboration with researchers from Duke University, have developed an unsupervised machine learning method, NashAE, for effective disentanglement of latent representations.
A team of researchers from the Oak Ridge National Laboratory (ORNL) developed a novel architecture for a hybrid quantum-classical neural network.
A group of ORNL researchers and collaborators have been working to develop a pipeline that simulates radiotherapy across different scales, e.g., the individual cellular scale, multicellular/tissue scale, organ scale, and whole-body scale.
A collaboration between scientists at Oak Ridge National Laboratory (ORNL) and University of Maryland/NIST developed a theoretical approach to combine different quantum noise reduction techniques to reduce the measurement-added noise in optomechanical s
A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) developed a new online heatmap method, named hilomap, to visualize geospatial datasets as online map layers when low and high trends are equally important to map users.