Filter News
Area of Research
- (-) Supercomputing (126)
- Advanced Manufacturing (21)
- Biology and Environment (112)
- Biology and Soft Matter (1)
- Building Technologies (2)
- Clean Energy (178)
- Climate and Environmental Systems (5)
- Computational Biology (1)
- Computational Engineering (3)
- Computer Science (15)
- Electricity and Smart Grid (2)
- Fuel Cycle Science and Technology (1)
- Fusion and Fission (10)
- Fusion Energy (4)
- Isotope Development and Production (1)
- Isotopes (4)
- Materials (113)
- Materials Characterization (1)
- Materials for Computing (20)
- Materials Under Extremes (1)
- Mathematics (1)
- National Security (56)
- Neutron Science (43)
- Nuclear Science and Technology (8)
- Quantum information Science (8)
- Sensors and Controls (1)
- Transportation Systems (1)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (5)
- (-) Big Data (18)
- (-) Computer Science (92)
- (-) Environment (20)
- (-) Grid (4)
- (-) Machine Learning (13)
- (-) Materials Science (15)
- (-) National Security (8)
- (-) Quantum Science (23)
- Advanced Reactors (1)
- Artificial Intelligence (34)
- Bioenergy (9)
- Biology (11)
- Biomedical (16)
- Biotechnology (2)
- Buildings (3)
- Chemical Sciences (5)
- Climate Change (17)
- Coronavirus (14)
- Critical Materials (3)
- Cybersecurity (8)
- Decarbonization (4)
- Energy Storage (7)
- Exascale Computing (20)
- Frontier (26)
- Fusion (1)
- High-Performance Computing (34)
- Isotopes (1)
- Materials (13)
- Mathematics (1)
- Microscopy (7)
- Molten Salt (1)
- Nanotechnology (11)
- Net Zero (1)
- Neutron Science (13)
- Nuclear Energy (4)
- Partnerships (1)
- Physics (7)
- Polymers (2)
- Quantum Computing (19)
- Security (5)
- Simulation (12)
- Software (1)
- Space Exploration (3)
- Summit (41)
- Sustainable Energy (9)
- Transportation (6)
Media Contacts
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.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
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.
Scientists’ increasing mastery of quantum mechanics is heralding a new age of innovation. Technologies that harness the power of nature’s most minute scale show enormous potential across the scientific spectrum
More than 50 current employees and recent retirees from ORNL received Department of Energy Secretary’s Honor Awards from Secretary Jennifer Granholm in January as part of project teams spanning the national laboratory system. The annual awards recognized 21 teams and three individuals for service and contributions to DOE’s mission and to the benefit of the nation.
A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
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