Computational scientists and neutron structural biologists from Oak Ridge National Laboratory developed an integrated workflow using small-angle neutron scattering (SANS), atomistic molecular dynamics (MD) simulation, and an autoencoder-based deep learn
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Large amounts of longitudinal, multimodal electronic health data are being produced from a variety of sources daily. If leveraged properly, these comprehensive data sources can be used for innovative precision medicine and precision public health.
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.
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.
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.
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.
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.