Since its inception in 2010, the program bolsters national scientific discovery by supporting early career researchers in fields pertaining to the Office of Science.
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.
OR-SAGE (Oak Ridge Siting Analysis for power Generation Expansion) can be used to identify sites where a prototype chemical reactor would be the best options with respect to different technology scenarios.
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.
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 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.
A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
A team including researchers from the Department of Energy’s 91°µÍø has developed a digital tool to better monitor a condition known as Barrett’s esophagus, which affects more than 3 million people in the United States.
Researchers at 91°µÍø have identified a statistical relationship between the growth of cities and the spread of paved surfaces like roads and sidewalks.