Filter News
Area of Research
- Advanced Manufacturing (2)
- Biology and Environment (23)
- Building Technologies (1)
- Clean Energy (31)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Computational Engineering (3)
- Computer Science (16)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (1)
- Fusion and Fission (3)
- Fusion Energy (2)
- Isotopes (1)
- Materials (22)
- Materials for Computing (7)
- Mathematics (1)
- National Security (25)
- Neutron Science (16)
- Nuclear Science and Technology (2)
- Quantum information Science (6)
- Supercomputing (109)
News Topics
- (-) Artificial Intelligence (87)
- (-) Computer Science (184)
- 3-D Printing/Advanced Manufacturing (116)
- Advanced Reactors (34)
- Big Data (50)
- Bioenergy (88)
- Biology (96)
- Biomedical (58)
- Biotechnology (21)
- Buildings (54)
- Chemical Sciences (59)
- Clean Water (29)
- Climate Change (94)
- Composites (25)
- Coronavirus (46)
- Critical Materials (24)
- Cybersecurity (35)
- Decarbonization (74)
- Education (3)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (107)
- Environment (192)
- Exascale Computing (36)
- Fossil Energy (5)
- Frontier (41)
- Fusion (53)
- Grid (61)
- High-Performance Computing (83)
- Hydropower (11)
- Irradiation (3)
- Isotopes (48)
- ITER (7)
- Machine Learning (46)
- Materials (140)
- Materials Science (134)
- Mathematics (6)
- Mercury (12)
- Microelectronics (2)
- Microscopy (50)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (59)
- Net Zero (12)
- Neutron Science (129)
- Nuclear Energy (105)
- Partnerships (40)
- Physics (59)
- Polymers (31)
- Quantum Computing (31)
- Quantum Science (66)
- Renewable Energy (2)
- Security (24)
- Simulation (45)
- Software (1)
- Space Exploration (24)
- Statistics (3)
- Summit (57)
- Sustainable Energy (120)
- Transformational Challenge Reactor (7)
- Transportation (93)
Media Contacts
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
Pablo Moriano, a research scientist in the Computer Science and Mathematics Division at ORNL, was selected as a member of the 2024 Class of MGB-SIAM Early Career Fellows.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.
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.
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.
The U.S. Department of Energy’s Oak Ridge Leadership Computing Facility has informed the recipients of high-performance computing time through the SummitPLUS allocation program, which extends the operation of the Summit supercomputer through October 2024.
A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.
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.