Robert Hettich: Decoding biological complexity with next-gen mass spectrometry
Filter News
Area of Research
News Type
News Topics
- (-) Fusion (2)
- (-) Isotopes (1)
- 3-D Printing/Advanced Manufacturing (3)
- Advanced Reactors (2)
- Artificial Intelligence (26)
- Big Data (32)
- Bioenergy (5)
- Biology (6)
- Biomedical (7)
- Biotechnology (3)
- Buildings (3)
- Chemical Sciences (2)
- Clean Water (3)
- Computer Science (36)
- Coronavirus (2)
- Cybersecurity (3)
- Emergency (1)
- Energy Storage (1)
- Environment (27)
- Exascale Computing (8)
- Frontier (8)
- Grid (6)
- High-Performance Computing (15)
- Hydropower (2)
- ITER (1)
- Machine Learning (13)
- Materials Science (6)
- Mathematics (2)
- Microscopy (2)
- Molten Salt (1)
- Nanotechnology (4)
- National Security (24)
- Neutron Science (2)
- Nuclear Energy (3)
- Physics (4)
- Quantum Science (1)
- Security (4)
- Simulation (6)
- Space Exploration (1)
- Statistics (2)
- Summit (10)
- Transportation (5)
Media Contacts

A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.

Equipment and expertise from Oak Ridge National Laboratory will allow scientists studying fusion energy and technologies to acquire crucial data during landmark fusion experiments in Europe.

Oak Ridge National Laboratory provided significant contributions and coordination in the development of the Nuclear Energy Agency’s (NEA’s) recently released Spent Fuel Isotopic Composition (SFCOMPO) 2.0—the world’s largest open database for spent