Filter News
Area of Research
- (-) Neutron Science (6)
- (-) Supercomputing (32)
- Biological Systems (1)
- Biology and Environment (51)
- Biology and Soft Matter (1)
- Clean Energy (19)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Fusion and Fission (2)
- Materials (11)
- Materials for Computing (2)
- National Security (11)
- Quantum information Science (2)
News Topics
- (-) Artificial Intelligence (22)
- (-) Bioenergy (5)
- (-) Biotechnology (1)
- (-) Climate Change (12)
- (-) Microscopy (2)
- 3-D Printing/Advanced Manufacturing (4)
- Big Data (13)
- Biology (6)
- Biomedical (10)
- Buildings (2)
- Chemical Sciences (1)
- Clean Water (2)
- Computer Science (47)
- Coronavirus (7)
- Cybersecurity (2)
- Decarbonization (4)
- Energy Storage (3)
- Environment (16)
- Exascale Computing (12)
- Fossil Energy (1)
- Frontier (13)
- Grid (1)
- High-Performance Computing (20)
- Machine Learning (9)
- Materials (9)
- Materials Science (13)
- Mathematics (1)
- Nanotechnology (6)
- National Security (3)
- Net Zero (1)
- Neutron Science (35)
- Nuclear Energy (3)
- Physics (4)
- Polymers (1)
- Quantum Computing (10)
- Quantum Science (10)
- Security (2)
- Simulation (10)
- Software (1)
- Space Exploration (2)
- Summit (21)
- Sustainable Energy (3)
- Transportation (4)
Media Contacts
![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.
![ORNL’s Melissa Allen-Dumas examines the ways global and regional climate models can shed light on local climate effects and inform equitable solutions. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-12/2021-P00300_0.jpg?h=8f9cfe54&itok=FYXNa_GE)
The world is full of “huge, gnarly problems,” as ORNL research scientist and musician Melissa Allen-Dumas puts it — no matter what line of work you’re in. That was certainly the case when she would wrestle with a tough piece of music.
![Deeksha Rastogi uses high-performance computing to understand the human impacts of climate change. Credit: Carlos Jones, ORNL/U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-09/2021-P06173_0.jpg?h=6881dff6&itok=IbKJui6N)
An international problem like climate change needs solutions that cross boundaries, both on maps and among disciplines. Oak Ridge National Laboratory computational scientist Deeksha Rastogi embodies that approach.
![An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-06/frame1.png?h=d1cb525d&itok=51pwBWyP)
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.
![ORNL’s Sergei Kalinin and Rama Vasudevan (foreground) use scanning probe microscopy to study bulk ferroelectricity and surface electrochemistry -- and generate a lot of data. Credit: Jason Richards/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2021-05/KalininVasudevan_2017-P03014_0.jpg?h=1116cd87&itok=KEEOB4hi)
At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.
![Blue sky above ORNL campus.](/sites/default/files/styles/list_page_thumbnail/public/2020-11/ORNLCampus1_0.jpg?h=85f71c8f&itok=Bic6TXC0)
ORNL and three partnering institutions have received $4.2 million over three years to apply artificial intelligence to the advancement of complex systems in which human decision making could be enhanced via technology.
![Coronavirus graphic](/sites/default/files/styles/list_page_thumbnail/public/2020-04/covid19_jh_0.png?h=d1cb525d&itok=PyngFUZw)
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.
![Scientists created a novel polymer that is as effective as natural proteins in transporting protons through a membrane. Credit: ORNL/Jill Hemman](/sites/default/files/styles/list_page_thumbnail/public/2020-03/19-G01195_nature_feature_0.png?h=e4fbc3eb&itok=K8czXmTr)
Biological membranes, such as the “walls” of most types of living cells, primarily consist of a double layer of lipids, or “lipid bilayer,” that forms the structure, and a variety of embedded and attached proteins with highly specialized functions, including proteins that rapidly and selectively transport ions and molecules in and out of the cell.
![The image visualizes how the team’s multitask convolutional neural network classifies primary cancer sites. Image credit: Hong-Jun Yoon/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2020-02/shot_0.png?h=49ab6177&itok=IXL5Ingy)
As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime.