Filter News
Area of Research
- (-) Supercomputing (66)
- Advanced Manufacturing (1)
- Biology and Environment (24)
- Building Technologies (1)
- Clean Energy (75)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (12)
- Electricity and Smart Grid (2)
- Energy Sciences (1)
- Fusion and Fission (17)
- Fusion Energy (11)
- Isotopes (1)
- Materials (28)
- Materials for Computing (7)
- Mathematics (1)
- National Security (21)
- Neutron Science (11)
- Nuclear Science and Technology (12)
- Quantum information Science (4)
- Sensors and Controls (1)
News Type
News Topics
- (-) Computer Science (62)
- (-) Energy Storage (2)
- (-) Fusion (1)
- (-) Grid (1)
- (-) Machine Learning (8)
- (-) Mathematics (1)
- 3-D Printing/Advanced Manufacturing (2)
- Advanced Reactors (1)
- Artificial Intelligence (22)
- Big Data (18)
- Bioenergy (3)
- Biology (7)
- Biomedical (11)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (2)
- Climate Change (14)
- Coronavirus (9)
- Critical Materials (3)
- Cybersecurity (2)
- Decarbonization (3)
- Environment (17)
- Exascale Computing (15)
- Frontier (15)
- High-Performance Computing (25)
- Isotopes (1)
- Materials (5)
- Materials Science (9)
- Microscopy (2)
- Nanotechnology (6)
- National Security (3)
- Net Zero (1)
- Neutron Science (6)
- Nuclear Energy (3)
- Physics (4)
- Polymers (2)
- Quantum Computing (14)
- Quantum Science (13)
- Security (1)
- Simulation (12)
- Software (1)
- Space Exploration (2)
- Summit (28)
- Sustainable Energy (4)
- Transportation (4)
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.
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.
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.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
The world is full of “huge, gnarly problems,” as ORNL research scientist and musician Melissa Allen-Dumas puts it — no matter what line of work you’re in. That was certainly the case when she would wrestle with a tough piece of music.
A team led by the U.S. Department of Energy’s Oak Ridge National Laboratory demonstrated the viability of a “quantum entanglement witness” capable of proving the presence of entanglement between magnetic particles, or spins, in a quantum material.
The daily traffic congestion along the streets and interstate lanes of Chattanooga could be headed the way of the horse and buggy with help from ORNL researchers.
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.