Filter News
Area of Research
- (-) Biological Systems (2)
- (-) Climate and Environmental Systems (4)
- (-) Nuclear Science and Technology (4)
- (-) Supercomputing (47)
- Advanced Manufacturing (3)
- Biology and Environment (100)
- Biology and Soft Matter (1)
- Clean Energy (81)
- Computational Engineering (2)
- Computer Science (7)
- Electricity and Smart Grid (2)
- Fusion and Fission (4)
- Fusion Energy (1)
- Materials (32)
- Materials for Computing (7)
- Mathematics (1)
- National Security (18)
- Neutron Science (12)
- Quantum information Science (3)
- Sensors and Controls (1)
News Type
News Topics
- (-) Bioenergy (5)
- (-) Environment (21)
- (-) Exascale Computing (13)
- (-) Frontier (14)
- (-) Grid (1)
- (-) Machine Learning (8)
- (-) Molten Salt (4)
- (-) Nanotechnology (6)
- (-) Net Zero (1)
- 3-D Printing/Advanced Manufacturing (4)
- Advanced Reactors (9)
- Artificial Intelligence (22)
- Big Data (17)
- Biology (8)
- Biomedical (13)
- Biotechnology (1)
- Buildings (2)
- Chemical Sciences (2)
- Climate Change (16)
- Computer Science (62)
- Coronavirus (9)
- Critical Materials (3)
- Cybersecurity (2)
- Decarbonization (3)
- Energy Storage (2)
- Fusion (8)
- High-Performance Computing (23)
- Isotopes (3)
- Materials (5)
- Materials Science (11)
- Mathematics (1)
- Microscopy (2)
- National Security (3)
- Neutron Science (8)
- Nuclear Energy (29)
- Physics (4)
- Polymers (2)
- Quantum Computing (14)
- Quantum Science (13)
- Security (1)
- Simulation (11)
- Software (1)
- Space Exploration (5)
- Summit (27)
- Sustainable Energy (4)
- Transformational Challenge Reactor (2)
- Transportation (4)
Media Contacts
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
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.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
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.
A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
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.
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.