![Weyl semimetal](/sites/default/files/styles/featured_square_large/public/2024-08/Picture4.jpg?h=b38bf506&itok=nYXXiLDs)
Filter News
Area of Research
News Topics
- (-) Artificial Intelligence (3)
- (-) Computer Science (6)
- (-) Environment (7)
- (-) Microscopy (2)
- 3-D Printing/Advanced Manufacturing (4)
- Big Data (1)
- Bioenergy (4)
- Biology (4)
- Biomedical (3)
- Buildings (9)
- Clean Water (1)
- Climate Change (7)
- Composites (2)
- Coronavirus (3)
- Critical Materials (1)
- Cybersecurity (1)
- Decarbonization (9)
- Energy Storage (5)
- Exascale Computing (2)
- Frontier (3)
- Grid (2)
- High-Performance Computing (5)
- Hydropower (1)
- Machine Learning (2)
- Materials (6)
- Materials Science (3)
- Nanotechnology (2)
- National Security (1)
- Net Zero (1)
- Neutron Science (1)
- Partnerships (1)
- Polymers (1)
- Quantum Computing (5)
- Quantum Science (3)
- Simulation (4)
- Summit (5)
- Sustainable Energy (7)
- Transportation (5)
Media Contacts
![This image illustrates lattice distortion, strain, and ion distribution in metal halide perovskites, which can be induced by external stimuli such as light and heat. Image credit: Stephen Jesse/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-03/FerroicHalidePerovskite.jpg?h=b803af89&itok=eBzxpb4b)
A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
![ORNL researchers worked with partners at the Colorado School of Mines and Baylor University to develop a new process optimization and control method for a closed-circuit reverse osmosis desalination system. The work is intended to support fully automated, decentralized water treatment plants. Credit: Andrew Sproles/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-02/seay_nawiStoryTip01-01_0.png?h=8f76a359&itok=1YanCIho)
Oak Ridge National Laboratory scientists worked with the Colorado School of Mines and Baylor University to develop and test control methods for autonomous water treatment plants that use less energy and generate less waste.
![Bruce Warmack is using his physics and electrical engineering expertise to analyze advanced sensors for the power grid on a new testbed he developed at the Distributed Energy Communications and Controls Laboratory at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-02/Warmack%202%202022-P00648.jpg?h=c6980913&itok=2uEUwtLk)
Bruce Warmack has been fascinated by science since his mother finally let him have a chemistry set at the age of nine. He’d been pestering her for one since he was six.
![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.
![Biofuels, such as those derived from the switchgrass being harvested in this field in Vonore, Tennessee, are just one of the technology-based solutions that ORNL summit participants identified recently as key to decarbonizing the agriculture sector. Credit: Erin G. Webb, ORNL/U.S. Dept. of Energy.](/sites/default/files/styles/list_page_thumbnail/public/2022-01/SwitchgrassVonore_ErinWebb_0.jpg?h=0ad68e88&itok=GNhjrsix)
Energy and sustainability experts from ORNL, industry, universities and the federal government recently identified key focus areas to meet the challenge of successfully decarbonizing the agriculture sector
![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.