Filter News
Area of Research
- (-) Supercomputing (50)
- Biological Systems (1)
- Biology and Environment (40)
- Clean Energy (28)
- Computational Biology (1)
- Electricity and Smart Grid (1)
- Fusion and Fission (19)
- Fusion Energy (4)
- Isotopes (6)
- Materials (27)
- Materials for Computing (2)
- National Security (13)
- Neutron Science (12)
- Nuclear Science and Technology (16)
- Quantum information Science (4)
News Topics
- (-) Artificial Intelligence (21)
- (-) Bioenergy (3)
- (-) Biomedical (7)
- (-) Exascale Computing (12)
- (-) Grid (1)
- (-) Nuclear Energy (2)
- (-) Physics (3)
- (-) Quantum Science (10)
- (-) Space Exploration (1)
- 3-D Printing/Advanced Manufacturing (2)
- Big Data (13)
- Biology (6)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (1)
- Climate Change (12)
- Computer Science (45)
- Coronavirus (7)
- Cybersecurity (2)
- Decarbonization (3)
- Energy Storage (1)
- Environment (13)
- Frontier (13)
- High-Performance Computing (20)
- Machine Learning (7)
- Materials (4)
- Materials Science (8)
- Mathematics (1)
- Microscopy (2)
- Nanotechnology (5)
- National Security (3)
- Net Zero (1)
- Neutron Science (6)
- Quantum Computing (10)
- Security (1)
- Simulation (10)
- Software (1)
- Summit (21)
- Sustainable Energy (3)
- Transportation (3)
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.
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 rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
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.
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.
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.
At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.