Filter News
Area of Research
- (-) Biology and Environment (27)
- (-) Biology and Soft Matter (1)
- Advanced Manufacturing (2)
- Clean Energy (122)
- Computational Engineering (1)
- Computer Science (10)
- Electricity and Smart Grid (3)
- Functional Materials for Energy (1)
- Fusion and Fission (15)
- Fusion Energy (7)
- Isotopes (1)
- Materials (93)
- Materials for Computing (12)
- National Security (30)
- Neutron Science (29)
- Nuclear Science and Technology (16)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (9)
- Sensors and Controls (2)
- Supercomputing (73)
- Transportation Systems (2)
News Topics
- (-) Advanced Reactors (1)
- (-) Chemical Sciences (12)
- (-) Exascale Computing (4)
- (-) Grid (3)
- (-) Machine Learning (8)
- (-) Molten Salt (1)
- (-) Physics (2)
- (-) Security (2)
- (-) Transportation (3)
- 3-D Printing/Advanced Manufacturing (11)
- Artificial Intelligence (9)
- Big Data (9)
- Bioenergy (45)
- Biology (73)
- Biomedical (16)
- Biotechnology (13)
- Buildings (2)
- Clean Water (11)
- Climate Change (41)
- Composites (5)
- Computer Science (19)
- Coronavirus (13)
- Critical Materials (1)
- Cybersecurity (1)
- Decarbonization (20)
- Energy Storage (7)
- Environment (90)
- Frontier (3)
- Fusion (1)
- High-Performance Computing (20)
- Hydropower (8)
- Isotopes (2)
- Materials (12)
- Materials Science (6)
- Mathematics (3)
- Mercury (7)
- Microscopy (10)
- Nanotechnology (7)
- National Security (3)
- Net Zero (2)
- Neutron Science (4)
- Nuclear Energy (1)
- Partnerships (5)
- Polymers (2)
- Renewable Energy (1)
- Simulation (14)
- Summit (10)
- Sustainable Energy (30)
- Transformational Challenge Reactor (1)
Media Contacts
Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity.
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.
Ten scientists from the Department of Energy’s Oak Ridge National Laboratory are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
As rising global temperatures alter ecosystems worldwide, the need to accurately simulate complex environmental processes under evolving conditions is more urgent than ever.
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.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
Seven ORNL scientists have been named among the 2020 Highly Cited Researchers list, according to Clarivate, a data analytics firm that specializes in scientific and academic research.
In the race to identify solutions to the COVID-19 pandemic, researchers at the Department of Energy’s Oak Ridge National Laboratory are joining the fight by applying expertise in computational science, advanced manufacturing, data science and neutron science.