Filter News
Area of Research
- (-) National Security (5)
- Advanced Manufacturing (1)
- Biology and Environment (32)
- Biology and Soft Matter (1)
- Clean Energy (27)
- Computational Biology (1)
- Computer Science (1)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (2)
- Fusion and Fission (7)
- Isotopes (2)
- Materials (20)
- Neutron Science (4)
- Supercomputing (13)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (1)
- (-) Biomedical (2)
- (-) Energy Storage (1)
- (-) Environment (1)
- (-) Summit (1)
- Artificial Intelligence (3)
- Big Data (2)
- Bioenergy (2)
- Biology (4)
- Biotechnology (1)
- Buildings (1)
- Chemical Sciences (2)
- Climate Change (4)
- Computer Science (7)
- Coronavirus (1)
- Cybersecurity (5)
- Decarbonization (1)
- Exascale Computing (1)
- Frontier (1)
- Grid (3)
- High-Performance Computing (1)
- Machine Learning (4)
- Materials (1)
- National Security (13)
- Neutron Science (2)
- Partnerships (1)
- Physics (1)
- Security (3)
- Simulation (1)
- Sustainable Energy (1)
Media Contacts
Scientists develop environmental justice lens to identify neighborhoods vulnerable to climate change
A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.