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).
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 multidisciplinary team of researchers from 91°µÍø 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.
Members and students of the Computational Urban Sciences group demonstrated a method for generating scenarios of urban neighborhood growth based on existing physical structures and placement of buildings in neighborhoods.
A multidisciplinary team of researchers from 91°µÍø (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.
We present an intercomparison of a suite of high-resolution downscaled climate projections based on a six-member General Climate Models (GCM) ensemble from the 6th Phase of Coupled Models Intercomparison Project (CMIP6).
A web-based GUI for INTERSECT has been created which allows a user to configure an experiment on an electron microscope, setting such parameters as maximum number of steps for the machine learning algorithm to perform.
Researchers at 91°µÍø developed a new parallel performance portable algorithm for solving the Euclidean minimum spanning tree problem (EMST), capable of processing tens of millions of data points a second.