Filter News
Area of Research
- (-) Biology and Environment (28)
- Advanced Manufacturing (6)
- Clean Energy (128)
- Computational Engineering (1)
- Computer Science (10)
- Electricity and Smart Grid (3)
- Energy Frontier Research Centers (1)
- Fuel Cycle Science and Technology (1)
- Functional Materials for Energy (1)
- Fusion and Fission (28)
- Fusion Energy (13)
- Isotope Development and Production (1)
- Isotopes (25)
- Materials (125)
- Materials Characterization (1)
- Materials for Computing (22)
- Materials Under Extremes (1)
- National Security (49)
- Neutron Science (39)
- Nuclear Science and Technology (17)
- Quantum information Science (9)
- Sensors and Controls (1)
- Supercomputing (65)
- Transportation Systems (2)
News Topics
- (-) Fusion (1)
- (-) Grid (3)
- (-) Isotopes (2)
- (-) Machine Learning (8)
- (-) Materials Science (6)
- (-) Nanotechnology (7)
- (-) National Security (3)
- (-) Transformational Challenge Reactor (1)
- (-) Transportation (3)
- 3-D Printing/Advanced Manufacturing (11)
- Advanced Reactors (1)
- Artificial Intelligence (9)
- Big Data (9)
- Bioenergy (45)
- Biology (73)
- Biomedical (16)
- Biotechnology (13)
- Buildings (2)
- Chemical Sciences (11)
- Clean Water (11)
- Climate Change (40)
- Composites (5)
- Computer Science (19)
- Coronavirus (13)
- Critical Materials (1)
- Cybersecurity (1)
- Decarbonization (19)
- Energy Storage (7)
- Environment (89)
- Exascale Computing (4)
- Frontier (3)
- High-Performance Computing (20)
- Hydropower (8)
- Materials (12)
- Mathematics (3)
- Mercury (7)
- Microscopy (10)
- Molten Salt (1)
- Net Zero (2)
- Neutron Science (4)
- Nuclear Energy (1)
- Partnerships (5)
- Physics (2)
- Polymers (2)
- Renewable Energy (1)
- Security (2)
- Simulation (14)
- Summit (10)
- Sustainable Energy (30)
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.
The Department of Energy’s Office of Science has selected five Oak Ridge National Laboratory scientists for Early Career Research Program awards.
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.
The annual Director's Awards recognized four individuals and teams including awards for leadership in quantum simulation development and application on high-performance computing platforms, and revolutionary advancements in the area of microbial
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.