Filter News
Area of Research
- (-) Supercomputing (47)
- Advanced Manufacturing (2)
- Biology and Environment (62)
- Biology and Soft Matter (1)
- Clean Energy (82)
- Climate and Environmental Systems (3)
- Computational Engineering (2)
- Computer Science (6)
- Electricity and Smart Grid (3)
- Energy Frontier Research Centers (1)
- Functional Materials for Energy (1)
- Fusion and Fission (5)
- Isotope Development and Production (1)
- Isotopes (5)
- Materials (54)
- Materials for Computing (9)
- Mathematics (1)
- National Security (31)
- Neutron Science (18)
- Nuclear Science and Technology (9)
- Quantum information Science (2)
- Sensors and Controls (2)
News Topics
- (-) Climate Change (17)
- (-) Grid (5)
- (-) Machine Learning (14)
- (-) Molten Salt (1)
- (-) Nanotechnology (11)
- (-) Security (5)
- (-) Space Exploration (3)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (1)
- Artificial Intelligence (36)
- Big Data (19)
- Bioenergy (9)
- Biology (11)
- Biomedical (17)
- Biotechnology (2)
- Buildings (4)
- Chemical Sciences (5)
- Computer Science (95)
- Coronavirus (14)
- Critical Materials (3)
- Cybersecurity (8)
- Decarbonization (5)
- Energy Storage (8)
- Environment (21)
- Exascale Computing (22)
- Frontier (28)
- Fusion (1)
- High-Performance Computing (38)
- Isotopes (1)
- Materials (15)
- Materials Science (16)
- Mathematics (1)
- Microscopy (7)
- National Security (8)
- Net Zero (1)
- Neutron Science (13)
- Nuclear Energy (4)
- Partnerships (1)
- Physics (7)
- Polymers (2)
- Quantum Computing (19)
- Quantum Science (24)
- Simulation (14)
- Software (1)
- Summit (42)
- Sustainable Energy (10)
- Transportation (6)
Media Contacts
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
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.
More than 50 current employees and recent retirees from ORNL received Department of Energy Secretary’s Honor Awards from Secretary Jennifer Granholm in January as part of project teams spanning the national laboratory system. The annual awards recognized 21 teams and three individuals for service and contributions to DOE’s mission and to the benefit of the nation.
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.
An international problem like climate change needs solutions that cross boundaries, both on maps and among disciplines. Oak Ridge National Laboratory computational scientist Deeksha Rastogi embodies that approach.