Filter News
Area of Research
News Topics
- (-) Exascale Computing (37)
- (-) Microscopy (51)
- (-) Renewable Energy (2)
- 3-D Printing/Advanced Manufacturing (121)
- Advanced Reactors (34)
- Artificial Intelligence (91)
- 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 (26)
- Cybersecurity (35)
- Decarbonization (79)
- Education (4)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (108)
- Environment (194)
- 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 (144)
- Materials Science (140)
- Mathematics (7)
- Mercury (12)
- Microelectronics (3)
- Molten Salt (8)
- Nanotechnology (60)
- National Security (61)
- Net Zero (13)
- Neutron Science (131)
- Nuclear Energy (108)
- Partnerships (44)
- Physics (61)
- Polymers (33)
- Quantum Computing (34)
- Quantum Science (69)
- Security (24)
- Simulation (47)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (57)
- Sustainable Energy (125)
- Transformational Challenge Reactor (7)
- Transportation (97)
Media Contacts
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
Researchers set a new benchmark for future experiments making materials in space rather than for space. They discovered that many kinds of glass have similar atomic structure and arrangements and can successfully be made in space. Scientists from nine institutions in government, academia and industry participated in this 5-year study.
A team led by researchers at ORNL explored training strategies for one of the largest artificial intelligence models to date with help from the world’s fastest supercomputer. The findings could help guide training for a new generation of AI models for scientific research.
When scientists pushed the world’s fastest supercomputer to its limits, they found those limits stretched beyond even their biggest expectations. In the latest milestone, a team of engineers and scientists used Frontier to simulate a system of nearly half a trillion atoms — the largest system ever modeled and more than 400 times the size of the closest competition.
Integral to the functionality of ORNL's Frontier supercomputer is its ability to store the vast amounts of data it produces onto its file system, Orion. But even more important to the computational scientists running simulations on Frontier is their capability to quickly write and read to Orion along with effectively analyzing all that data. And that’s where ADIOS comes in.
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.
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
A team of eight scientists won the Association for Computing Machinery’s 2023 Gordon Bell Prize for their study that used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
Researchers used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.