Filter News
Area of Research
News Topics
- Artificial Intelligence (3)
- Big Data (1)
- Bioenergy (2)
- Biology (3)
- Biomedical (2)
- Buildings (1)
- Climate Change (3)
- Computer Science (4)
- Coronavirus (2)
- Decarbonization (1)
- Environment (2)
- Exascale Computing (1)
- Frontier (3)
- High-Performance Computing (4)
- Machine Learning (2)
- Materials (4)
- Materials Science (3)
- Microscopy (1)
- Nanotechnology (2)
- National Security (1)
- Neutron Science (1)
- Quantum Computing (5)
- Quantum Science (3)
- Simulation (3)
- Summit (4)
- Sustainable Energy (1)
Media Contacts
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.
Lawrence Berkeley National Laboratory physicists Christian Bauer, Marat Freytsis and Benjamin Nachman have leveraged an IBM Q quantum computer through the Oak Ridge Leadership Computing Facility’s Quantum Computing User Program to capture part of a
Computational users at the Oak Ridge Leadership Computing Facility, or OLCF, are running scientific codes on Frontier’s architecture in the form of a powerful test system at the OLCF called Crusher.
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.
Every day, hundreds of thousands of commuters across the country travel from houses, apartments and other residential spaces to commercial buildings — from offices and schools to gyms and grocery stores.
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.
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 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.