Filter News
Area of Research
- (-) Supercomputing (14)
- Biology and Environment (33)
- Biology and Soft Matter (1)
- Clean Energy (26)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Isotopes (2)
- Materials (7)
- Materials for Computing (3)
- National Security (9)
- Neutron Science (3)
- Nuclear Science and Technology (1)
- Quantum information Science (1)
News Topics
- (-) Bioenergy (2)
- (-) Biomedical (9)
- (-) Climate Change (3)
- (-) Cybersecurity (1)
- 3-D Printing/Advanced Manufacturing (1)
- Artificial Intelligence (7)
- Big Data (9)
- Biology (4)
- Buildings (1)
- Computer Science (26)
- Coronavirus (7)
- Critical Materials (1)
- Decarbonization (1)
- Environment (3)
- Exascale Computing (4)
- Frontier (4)
- Fusion (1)
- Grid (1)
- High-Performance Computing (6)
- Machine Learning (5)
- Materials (4)
- Materials Science (7)
- Mathematics (1)
- Microscopy (1)
- Nanotechnology (3)
- National Security (1)
- Neutron Science (4)
- Physics (1)
- Polymers (1)
- Quantum Computing (5)
- Quantum Science (7)
- Simulation (4)
- Summit (13)
- Sustainable Energy (2)
Media Contacts
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.
To optimize biomaterials for reliable, cost-effective paper production, building construction, and biofuel development, researchers often study the structure of plant cells using techniques such as freezing plant samples or placing them in a vacuum.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
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.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
A multi-institutional team, led by a group of investigators at Oak Ridge National Laboratory, has been studying various SARS-CoV-2 protein targets, including the virus’s main protease. The feat has earned the team a finalist nomination for the Association of Computing Machinery, or ACM, Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.
Scientists from Oak Ridge National Laboratory used high-performance computing to create protein models that helped reveal how the outer membrane is tethered to the cell membrane in certain bacteria.
From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.