Filter News
Area of Research
- (-) National Security (19)
- (-) Supercomputing (47)
- Advanced Manufacturing (6)
- Biology and Environment (82)
- Biology and Soft Matter (1)
- Clean Energy (95)
- Climate and Environmental Systems (4)
- Computational Biology (2)
- Computational Engineering (1)
- Computer Science (2)
- Fusion and Fission (19)
- Fusion Energy (11)
- Isotopes (18)
- Materials (60)
- Materials for Computing (7)
- Mathematics (1)
- Neutron Science (58)
- Nuclear Science and Technology (13)
- Quantum information Science (1)
- Transportation Systems (2)
News Type
News Topics
- (-) Cybersecurity (9)
- (-) Environment (20)
- (-) Fusion (1)
- (-) High-Performance Computing (25)
- (-) Neutron Science (7)
- (-) Physics (3)
- (-) Polymers (2)
- (-) Transportation (5)
- 3-D Printing/Advanced Manufacturing (3)
- Advanced Reactors (1)
- Artificial Intelligence (26)
- Big Data (20)
- Bioenergy (4)
- Biology (9)
- Biomedical (11)
- Biotechnology (2)
- Buildings (2)
- Chemical Sciences (2)
- Climate Change (17)
- Computer Science (66)
- Coronavirus (11)
- Critical Materials (3)
- Decarbonization (4)
- Energy Storage (3)
- Exascale Computing (13)
- Frontier (14)
- Grid (6)
- Machine Learning (14)
- Materials (6)
- Materials Science (10)
- Mathematics (1)
- Microscopy (2)
- Nanotechnology (6)
- National Security (23)
- Net Zero (1)
- Nuclear Energy (4)
- Quantum Computing (14)
- Quantum Science (14)
- Security (7)
- Simulation (11)
- Software (1)
- Space Exploration (2)
- Summit (27)
- Sustainable Energy (5)
Media Contacts
![ORNL will use its land surface modeling tools to determine Baltimore’s climate risk and analyze green infrastructure improvements that can help mitigate impacts on underserved communities as part of a DOE Urban Integrated Field Laboratory project. Source: Google Earth, accessed Sept. 12, 2022](/sites/default/files/styles/list_page_thumbnail/public/2022-09/baltimore_google_earth_0.png?h=252f27fa&itok=ZR6CzNnw)
ORNL researchers are deploying their broad expertise in climate data and modeling to create science-based mitigation strategies for cities stressed by climate change as part of two U.S. Department of Energy Urban Integrated Field Laboratory projects.
![Distinguished staff fellow Gang Seob “GS” Jung knew from an early age he wanted to be a scientist. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-09/seob2_0.jpg?h=264d153a&itok=J1cbkCWw)
Gang Seob “GS” Jung has known from the time he was in middle school that he was interested in science.
![ORNL identity science researcher Nell Barber works on a facial recognition camera. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-07/Picture1.jpg?h=47322e82&itok=CTajZTiF)
Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
![ORNL scientists created a geodemographic cluster for the Atlanta metro area that identifies risk factors related to climate impacts. Credit: ORNL/U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-06/climateMapCorrection_0.jpg?h=5c1b3784&itok=ijIvJETa)
Scientists develop environmental justice lens to identify neighborhoods vulnerable to climate change
A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.
![Logan Sturm, Alvin M. Weinberg Fellow at ORNL, creates a mashup between additive manufacturing and cybersecurity research. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/sturm-lab.jpg?h=1de2f7a8&itok=nYiuVTGx)
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
![LandScan Global depicts population distribution estimates across the planet. The darker orange and red colors above indicate higher population density. Credit: ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/Picture1_0.jpg?h=9d172ced&itok=uYwYp-pW)
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
![The ORNL researchers’ findings may enable better detection of uranium tetrafluoride hydrate, a little-studied byproduct of the nuclear fuel cycle, and better understanding of how environmental conditions influence the chemical behavior of fuel cycle materials. Credit: Kevin Pastoor/Colorado School of Mines](/sites/default/files/styles/list_page_thumbnail/public/2022-05/UF4%20hydrate.png?h=d318f057&itok=spT-Dg48)
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
![Genetic analysis revealed connections between inflammatory activity and development of atomic dermatitis, according to researchers from the UPenn School of Medicine, the Perelman School of Medicine, and Oak Ridge National Laboratory. Credit: Kang Ko/UPenn](/sites/default/files/styles/list_page_thumbnail/public/2022-02/Graves-AD_0.jpg?h=46d8a70d&itok=77AW7Swv)
University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.
![The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/E3SM_0.jpg?h=d5571230&itok=lKS66vCl)
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
![This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/thermosipho_collabfold2_0.jpg?h=3432ff3c&itok=4xhLbjKZ)
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.