
Filter News
Area of Research
- Advanced Manufacturing (1)
- Biology and Environment (8)
- Building Technologies (1)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (10)
- Energy Science (37)
- Fusion and Fission (2)
- Fusion Energy (6)
- Isotopes (1)
- Materials (16)
- Materials for Computing (3)
- Mathematics (1)
- National Security (3)
- Neutron Science (24)
- Nuclear Science and Technology (4)
- Quantum information Science (3)
- Supercomputing (17)
- Transportation Systems (2)
News Type
News Topics
- (-) Artificial Intelligence (16)
- (-) Biomedical (11)
- (-) Clean Water (14)
- (-) Computer Science (42)
- (-) Fusion (9)
- (-) Neutron Science (27)
- (-) Security (1)
- (-) Transportation (36)
- 3-D Printing/Advanced Manufacturing (34)
- Advanced Reactors (13)
- Big Data (17)
- Bioenergy (17)
- Biology (21)
- Biotechnology (4)
- Buildings (21)
- Chemical Sciences (13)
- Composites (11)
- Coronavirus (11)
- Critical Materials (12)
- Cybersecurity (3)
- Emergency (1)
- Energy Storage (32)
- Environment (48)
- Exascale Computing (1)
- Fossil Energy (1)
- Frontier (1)
- Grid (22)
- High-Performance Computing (12)
- Hydropower (6)
- Irradiation (2)
- Isotopes (5)
- ITER (3)
- Machine Learning (14)
- Materials (36)
- Materials Science (34)
- Mathematics (3)
- Mercury (3)
- Microscopy (11)
- Molten Salt (5)
- Nanotechnology (12)
- National Security (3)
- Nuclear Energy (19)
- Partnerships (2)
- Physics (4)
- Polymers (10)
- Quantum Computing (5)
- Quantum Science (11)
- Simulation (9)
- Space Exploration (10)
- Statistics (1)
- Summit (8)
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.

Researchers at ORNL have developed a tool that gives builders a quick way to measure, correct and certify level foundations. FLAT, or the Flat and Level Analysis Tool, examines a 360-degree laser scan of a construction site using ORNL-developed segmentation algorithms and machine learning to locate uneven areas on a concrete slab.

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.

Researchers have identified a molecule essential for the microbial conversion of inorganic mercury into the neurotoxin methylmercury, moving closer to blocking the dangerous pollutant before it forms.

In a game-changing study, ORNL scientists developed a deep learning model — a type of artificial intelligence that mimics human brain function — to analyze high-speed videos of plasma plumes during a process called pulsed laser deposition.

ORNL has partnered with Western Michigan University to advance intelligent road infrastructure through the development of new chip-enabled raised pavement markers. These innovative markers transmit lane-keeping information to passing vehicles, enhancing safety and enabling smarter driving in all weather conditions.

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.

Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.

An international team using neutrons set the first benchmark (one nanosecond) for a polymer-electrolyte and lithium-salt mixture. Findings could produce safer, more powerful lithium batteries.