Filter News
Area of Research
- Advanced Manufacturing (1)
- Biological Systems (1)
- Biology and Environment (33)
- Biology and Soft Matter (1)
- Clean Energy (67)
- Computational Biology (1)
- Computational Engineering (1)
- Computer Science (1)
- Fusion and Fission (14)
- Fusion Energy (1)
- Isotope Development and Production (1)
- Isotopes (6)
- Materials (87)
- Materials Characterization (1)
- Materials for Computing (13)
- Materials Under Extremes (1)
- National Security (7)
- Neutron Science (32)
- Nuclear Science and Technology (9)
- Supercomputing (55)
News Type
News Topics
- (-) Advanced Reactors (18)
- (-) Biomedical (45)
- (-) Chemical Sciences (51)
- (-) Energy Storage (69)
- (-) Frontier (38)
- (-) Materials Science (94)
- 3-D Printing/Advanced Manufacturing (81)
- Artificial Intelligence (75)
- Big Data (30)
- Bioenergy (74)
- Biology (80)
- Biotechnology (18)
- Buildings (31)
- Clean Water (15)
- Climate Change (70)
- Composites (15)
- Computer Science (139)
- Coronavirus (34)
- Critical Materials (13)
- Cybersecurity (31)
- Decarbonization (64)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Environment (137)
- Exascale Computing (34)
- Fossil Energy (5)
- Fusion (43)
- Grid (38)
- High-Performance Computing (69)
- Hydropower (5)
- Isotopes (45)
- ITER (4)
- Machine Learning (35)
- Materials (100)
- Mathematics (6)
- Mercury (9)
- Microelectronics (3)
- Microscopy (36)
- Molten Salt (3)
- Nanotechnology (42)
- National Security (53)
- Net Zero (11)
- Neutron Science (96)
- Nuclear Energy (80)
- Partnerships (43)
- Physics (52)
- Polymers (20)
- Quantum Computing (29)
- Quantum Science (56)
- Renewable Energy (2)
- Security (22)
- Simulation (38)
- Software (1)
- Space Exploration (15)
- Statistics (2)
- Summit (50)
- Sustainable Energy (74)
- Transformational Challenge Reactor (7)
- Transportation (52)
Media Contacts
The U.S. Department of Energy announced funding for 12 projects with private industry to enable collaboration with DOE national laboratories on overcoming challenges in fusion energy development.
Two of the researchers who share the Nobel Prize in Chemistry announced Wednesday—John B. Goodenough of the University of Texas at Austin and M. Stanley Whittingham of Binghamton University in New York—have research ties to ORNL.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
Quanex Building Products has signed a non-exclusive agreement to license a method to produce insulating material from ORNL. The low-cost material can be used as an additive to increase thermal insulation performance and improve energy efficiency when applied to a variety of building products.
Ask Tyler Gerczak to find a negative in working at the Department of Energy’s Oak Ridge National Laboratory, and his only complaint is the summer weather. It is not as forgiving as the summers in Pulaski, Wisconsin, his hometown.
A team led by scientists at the Department of Energy’s Oak Ridge National Laboratory explored how atomically thin two-dimensional (2D) crystals can grow over 3D objects and how the curvature of those objects can stretch and strain the
OAK RIDGE, Tenn., May 7, 2019—The U.S. Department of Energy today announced a contract with Cray Inc. to build the Frontier supercomputer at Oak Ridge National Laboratory, which is anticipated to debut in 2021 as the world’s most powerful computer with a performance of greater than 1.5 exaflops.
OAK RIDGE, Tenn., May 7, 2019—Energy Secretary Rick Perry, Congressman Chuck Fleischmann and lab officials today broke ground on a multipurpose research facility that will provide state-of-the-art laboratory space
Ionic conduction involves the movement of ions from one location to another inside a material. The ions travel through point defects, which are irregularities in the otherwise consistent arrangement of atoms known as the crystal lattice. This sometimes sluggish process can limit the performance and efficiency of fuel cells, batteries, and other energy storage technologies.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.