Filter News
Area of Research
- Advanced Manufacturing (7)
- Biology and Environment (19)
- Clean Energy (40)
- Computational Biology (1)
- Computational Engineering (1)
- Computer Science (6)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (2)
- Fusion and Fission (2)
- Fusion Energy (1)
- Isotopes (4)
- Materials (78)
- Materials Characterization (2)
- Materials for Computing (11)
- Materials Under Extremes (1)
- National Security (14)
- Neutron Science (20)
- Supercomputing (65)
News Topics
- (-) Artificial Intelligence (91)
- (-) Materials (143)
- (-) Quantum Computing (34)
- 3-D Printing/Advanced Manufacturing (120)
- Advanced Reactors (34)
- Big Data (53)
- Bioenergy (91)
- Biology (98)
- Biomedical (58)
- Biotechnology (22)
- Buildings (57)
- Chemical Sciences (63)
- Clean Water (29)
- Climate Change (99)
- Composites (26)
- Computer Science (187)
- Coronavirus (46)
- Critical Materials (25)
- Cybersecurity (35)
- Decarbonization (79)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (108)
- Environment (194)
- Exascale Computing (37)
- Fossil Energy (5)
- Frontier (42)
- Fusion (54)
- Grid (62)
- High-Performance Computing (84)
- Hydropower (11)
- Irradiation (3)
- Isotopes (53)
- ITER (7)
- Machine Learning (47)
- Materials Science (139)
- Mathematics (7)
- Mercury (12)
- Microelectronics (3)
- Microscopy (51)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (61)
- Net Zero (13)
- Neutron Science (131)
- Nuclear Energy (107)
- Partnerships (43)
- Physics (61)
- Polymers (33)
- Quantum Science (69)
- Renewable Energy (2)
- Security (24)
- Simulation (47)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (57)
- Sustainable Energy (125)
- Transformational Challenge Reactor (7)
- Transportation (97)
Media Contacts
Researchers at the Department of Energy’s Oak Ridge and Lawrence Berkeley National Laboratories are evolving graph neural networks to scale on the nation’s most powerful computational resources, a necessary step in tackling today’s data-centric
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
ORNL’s successes in QIS and its forward-looking strategy were recently recognized in the form of three funding awards that will help ensure the laboratory remains a leader in advancing quantum computers and networks.
Corning uses neutron scattering to study the stability of different types of glass. Recently, researchers for the company have found that understanding the stability of the rings of atoms in glass materials can help predict the performance of glass products.
In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.
Scientists at ORNL have developed a technique for recovering and recycling critical materials that has garnered special recognition from a peer-reviewed materials journal and received a new phase of funding for research and development.
Rigoberto “Gobet” Advincula, a scientist at the Department of Energy’s Oak Ridge National Laboratory, has been named a 2023 Fellow of the National Academy of Inventors. Advincula has been recognized for his 14 patents and 21 published filings related to nanomaterials, smart coatings and films, solid-state device fabrication and chemical additives.
Ateios Systems licensed an ORNL technology for solvent-free battery component production using electron curing. Through Innovation Crossroads, Ateios continues to work with ORNL to enable readiness for production-quality battery components.
Despite its futuristic essence, artificial intelligence has a history that can be traced through several decades, and the ORNL has played a major role. From helping to drive fundamental and applied AI research from the field’s early days focused on expert systems, computer programs that rely on AI, to more recent developments in deep learning, a form of AI that enables machines to make evidence-based decisions, the lab’s AI research spans the spectrum.
A team of researchers from the University of Southern California, the Renaissance Computing Institute at the University of North Carolina, and Oak Ridge, Lawrence Berkeley and Argonne National Laboratories have received a grant from the U.S. Department of Energy to develop the fundamentals of a computational platform that is fault tolerant, robust to various environmental conditions and adaptive to workloads and resource availability.