
Filter News
Area of Research
- Advanced Manufacturing (3)
- Biology and Environment (4)
- Building Technologies (1)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (10)
- Energy Science (24)
- Fusion Energy (2)
- Isotopes (3)
- Materials (18)
- Materials for Computing (4)
- Mathematics (1)
- National Security (4)
- Neutron Science (2)
- Nuclear Science and Technology (3)
- Quantum information Science (3)
- Supercomputing (19)
News Type
News Topics
- (-) Composites (11)
- (-) Computer Science (42)
- (-) Cybersecurity (3)
- (-) Isotopes (5)
- (-) Microscopy (11)
- (-) Polymers (10)
- (-) Security (1)
- (-) Space Exploration (10)
- (-) Summit (8)
- 3-D Printing/Advanced Manufacturing (34)
- Advanced Reactors (13)
- Artificial Intelligence (16)
- Big Data (17)
- Bioenergy (17)
- Biology (21)
- Biomedical (11)
- Biotechnology (4)
- Buildings (21)
- Chemical Sciences (13)
- Clean Water (14)
- Coronavirus (11)
- Critical Materials (12)
- Emergency (1)
- Energy Storage (32)
- Environment (48)
- Exascale Computing (1)
- Fossil Energy (1)
- Frontier (1)
- Fusion (9)
- Grid (22)
- High-Performance Computing (12)
- Hydropower (6)
- Irradiation (2)
- ITER (3)
- Machine Learning (14)
- Materials (36)
- Materials Science (34)
- Mathematics (3)
- Mercury (3)
- Molten Salt (5)
- Nanotechnology (12)
- National Security (3)
- Neutron Science (27)
- Nuclear Energy (19)
- Partnerships (2)
- Physics (4)
- Quantum Computing (5)
- Quantum Science (12)
- Simulation (9)
- Statistics (1)
- Transportation (36)
Media Contacts

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications.

Scientists at ORNL have developed a vacuum-assisted extrusion method that reduces internal porosity by up to 75% in large-scale 3D-printed polymer parts. This new technique addresses the critical issue of porosity in large-scale prints but also paves the way for stronger composites.

Researchers at Stanford University, the European Center for Medium-Range Weather Forecasts, or ECMWF, and ORNL used the lab’s Summit supercomputer to better understand atmospheric gravity waves, which influence significant weather patterns that are difficult to forecast.

The ForWarn visualization tool was co-developed by ORNL with the U.S. Forest Service. The tool captures and analyzes satellite imagery to track impacts such as storms, wildfire and pests on forests across the nation.

Oak Ridge National Laboratory researchers are using a new bioderived material to 3D print custom roosting structures for endangered bats.

Oak Ridge National Laboratory scientists have developed a method leveraging artificial intelligence to accelerate the identification of environmentally friendly solvents for industrial carbon capture, biomass processing, rechargeable batteries and other applications.

Oak Ridge National Laboratory scientists ingeniously created a sustainable, soft material by combining rubber with woody reinforcements and incorporating “smart” linkages between the components that unlock on demand.

Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.

Oak Ridge National Laboratory scientists exploring bioenergy plant genetics have made a surprising discovery: a protein domain that could lead to new COVID-19 treatments.

Scientists at ORNL developed a competitive, eco-friendly alternative made without harmful blowing agents.