Evaluate the historical performance and future projections of compound heatwave and drought (CHD) extremes across the contiguous United States using CMIP6 global climate models, providing insights for regional adaptation strategies in response to
The objective of this study is to explore and analyze the spatial patterning of sociodemographic disparities in extreme heat exposure across multiple scales within the Conterminous United States (CONUS).
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
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.
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.
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