Filter News
Area of Research
- (-) National Security (14)
- (-) Supercomputing (69)
- Advanced Manufacturing (7)
- Biological Systems (1)
- Biology and Environment (24)
- Building Technologies (1)
- Clean Energy (39)
- Climate and Environmental Systems (1)
- Computational Biology (2)
- Computational Engineering (2)
- Computer Science (12)
- Fusion and Fission (18)
- Fusion Energy (10)
- Isotopes (8)
- Materials (57)
- Materials for Computing (13)
- Mathematics (1)
- Neutron Science (61)
- Nuclear Science and Technology (30)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (4)
- Transportation Systems (1)
News Type
News Topics
- (-) Biomedical (11)
- (-) Computer Science (66)
- (-) Materials Science (10)
- (-) Net Zero (1)
- (-) Neutron Science (7)
- (-) Nuclear Energy (4)
- (-) Space Exploration (2)
- 3-D Printing/Advanced Manufacturing (3)
- Advanced Reactors (1)
- Artificial Intelligence (26)
- Big Data (20)
- Bioenergy (4)
- Biology (9)
- Biotechnology (2)
- Buildings (2)
- Chemical Sciences (2)
- Climate Change (17)
- Coronavirus (11)
- Critical Materials (3)
- Cybersecurity (9)
- Decarbonization (4)
- Energy Storage (3)
- Environment (20)
- Exascale Computing (13)
- Frontier (14)
- Fusion (1)
- Grid (6)
- High-Performance Computing (25)
- Machine Learning (14)
- Materials (6)
- Mathematics (1)
- Microscopy (2)
- Nanotechnology (6)
- National Security (23)
- Partnerships (1)
- Physics (3)
- Polymers (2)
- Quantum Computing (14)
- Quantum Science (14)
- Security (7)
- Simulation (11)
- Software (1)
- Summit (27)
- Sustainable Energy (5)
- Transportation (5)
Media Contacts
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
Cameras see the world differently than humans. Resolution, equipment, lighting, distance and atmospheric conditions can impact how a person interprets objects on a photo.
When the COVID-19 pandemic stunned the world in 2020, researchers at ORNL wondered how they could extend their support and help
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 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.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.