Filter News
Area of Research
- Advanced Manufacturing (8)
- Biological Systems (1)
- Biology and Environment (2)
- Building Technologies (1)
- Clean Energy (27)
- Computational Engineering (1)
- Computer Science (5)
- Fusion Energy (4)
- Materials (23)
- National Security (2)
- Neutron Science (6)
- Nuclear Science and Technology (5)
- Sensors and Controls (1)
- Supercomputing (11)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (20)
- (-) Advanced Reactors (8)
- (-) Artificial Intelligence (13)
- (-) Biomedical (9)
- (-) Machine Learning (5)
- (-) Microscopy (10)
- (-) Security (9)
- (-) Sustainable Energy (8)
- Big Data (8)
- Bioenergy (11)
- Biology (1)
- Biotechnology (1)
- Clean Water (6)
- Climate Change (1)
- Composites (5)
- Computer Science (49)
- Critical Materials (1)
- Cybersecurity (8)
- Energy Storage (10)
- Environment (22)
- Exascale Computing (2)
- Frontier (2)
- Fusion (9)
- Grid (8)
- Isotopes (7)
- Materials Science (30)
- Mercury (2)
- Molten Salt (5)
- Nanotechnology (15)
- Neutron Science (26)
- Nuclear Energy (27)
- Physics (15)
- Polymers (7)
- Quantum Science (13)
- Space Exploration (6)
- Summit (11)
- Transportation (19)
Media Contacts
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the
![ORNL researcher Miaofang Chi refines her microscopy techniques toward understanding how and why materials have certain properties. ORNL researcher Miaofang Chi refines her microscopy techniques toward understanding how and why materials have certain properties.](/sites/default/files/styles/list_page_thumbnail/public/M_Chi_casual_0.png?itok=uvQT5OzH)
Material surfaces and interfaces may appear flat and void of texture to the naked eye, but a view from the nanoscale reveals an intricate tapestry of atomic patterns that control the reactions between the material and its environment. Electron microscopy allows researchers to probe...