Filter News
Area of Research
- (-) Supercomputing (28)
- Advanced Manufacturing (4)
- Biology and Environment (32)
- Building Technologies (2)
- Clean Energy (68)
- Computational Engineering (1)
- Computer Science (8)
- Electricity and Smart Grid (2)
- Energy Sciences (1)
- Fusion and Fission (7)
- Fusion Energy (8)
- Materials (13)
- Materials for Computing (5)
- National Security (21)
- Neutron Science (5)
- Nuclear Science and Technology (11)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (7)
- Sensors and Controls (1)
News Topics
- (-) Advanced Reactors (1)
- (-) Cybersecurity (2)
- (-) Grid (1)
- (-) Machine Learning (8)
- (-) Quantum Science (13)
- (-) Sustainable Energy (4)
- 3-D Printing/Advanced Manufacturing (2)
- Artificial Intelligence (22)
- Big Data (18)
- Bioenergy (3)
- Biology (7)
- Biomedical (11)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (2)
- Climate Change (14)
- Computer Science (61)
- Coronavirus (9)
- Critical Materials (3)
- Decarbonization (3)
- Energy Storage (2)
- Environment (17)
- Exascale Computing (15)
- Frontier (15)
- Fusion (1)
- High-Performance Computing (25)
- Isotopes (1)
- Materials (5)
- Materials Science (9)
- Mathematics (1)
- Microscopy (2)
- Nanotechnology (6)
- National Security (3)
- Net Zero (1)
- Neutron Science (6)
- Nuclear Energy (3)
- Physics (4)
- Polymers (2)
- Quantum Computing (14)
- Security (1)
- Simulation (12)
- Software (1)
- Space Exploration (2)
- Summit (28)
- Transportation (4)
Media Contacts
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 rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
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.
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.
Twenty-seven ORNL researchers Zoomed into 11 middle schools across Tennessee during the annual Engineers Week in February. East Tennessee schools throughout Oak Ridge and Roane, Sevier, Blount and Loudon counties participated, with three West Tennessee schools joining in.
To better understand the spread of SARS-CoV-2, the virus that causes COVID-19, Oak Ridge National Laboratory researchers have harnessed the power of supercomputers to accurately model the spike protein that binds the novel coronavirus to a human cell receptor.
The Department of Energy has selected Oak Ridge National Laboratory to lead a collaboration charged with developing quantum technologies that will usher in a new era of innovation.
ORNL researchers have developed an intelligent power electronic inverter platform that can connect locally sited energy resources such as solar panels, energy storage and electric vehicles and smoothly interact with the utility power grid.
From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.