Filter News
Area of Research
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (1)
- (-) Biomedical (2)
- (-) Cybersecurity (1)
- (-) High-Performance Computing (4)
- (-) Machine Learning (2)
- (-) Nanotechnology (2)
- Artificial Intelligence (3)
- Big Data (1)
- Bioenergy (3)
- Biology (3)
- Buildings (4)
- Climate Change (7)
- Computer Science (4)
- Coronavirus (2)
- Decarbonization (8)
- Energy Storage (2)
- Environment (7)
- Exascale Computing (1)
- Frontier (3)
- Materials (5)
- Materials Science (3)
- Microscopy (2)
- National Security (1)
- Net Zero (1)
- Neutron Science (1)
- Partnerships (1)
- Quantum Computing (5)
- Quantum Science (3)
- Simulation (3)
- Summit (4)
- Sustainable Energy (6)
- Transportation (3)
Media Contacts
Gang Seob “GS” Jung has known from the time he was in middle school that he was interested in science.
A crowd of investors and supporters turned out for last week’s Innovation Crossroads Showcase at the Knoxville Chamber as part of Innov865 Week. Sponsored by ORNL and the Tennessee Advanced Energy Business Council, the event celebrated deep-tech entrepreneurs and the Oak Ridge Corridor as a growing energy innovation hub for the nation.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
When Andrew Sutton arrived at ORNL in late 2020, he knew the move would be significant in more ways than just a change in location.
A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant