Filter News
Area of Research
News Topics
- (-) 3-D Printing/Advanced Manufacturing (15)
- (-) Artificial Intelligence (25)
- (-) Isotopes (11)
- (-) Microscopy (2)
- (-) Nuclear Energy (10)
- (-) Space Exploration (3)
- Advanced Reactors (3)
- Big Data (12)
- Bioenergy (11)
- Biology (13)
- Biomedical (6)
- Biotechnology (5)
- Buildings (13)
- Chemical Sciences (15)
- Clean Water (4)
- Climate Change (21)
- Composites (6)
- Computer Science (23)
- Critical Materials (5)
- Decarbonization (21)
- Education (1)
- Emergency (1)
- Energy Storage (9)
- Environment (20)
- Exascale Computing (6)
- Fossil Energy (3)
- Frontier (7)
- Fusion (6)
- Grid (9)
- High-Performance Computing (16)
- ITER (1)
- Machine Learning (9)
- Materials (14)
- Materials Science (15)
- Mathematics (4)
- Microelectronics (1)
- Nanotechnology (2)
- National Security (18)
- Net Zero (6)
- Neutron Science (10)
- Partnerships (14)
- Physics (5)
- Polymers (5)
- Quantum Computing (11)
- Quantum Science (12)
- Security (2)
- Simulation (14)
- Statistics (2)
- Summit (5)
- Sustainable Energy (20)
- Transportation (12)
Media Contacts
Two ORNL teams recently completed Cohort 18 of Energy I-Corps, an immersive two-month training program where the scientists define their technology’s value propositions, conduct stakeholder discovery interviews and develop viable market pathways.
Researchers at the Department of Energy’s Oak Ridge National Laboratory and partner institutions have launched a project to develop an innovative suite of tools that will employ machine learning algorithms for more effective cybersecurity analysis of the U.S. power grid.
Brittany Rodriguez never imagined she would pursue a science career at a Department of Energy national laboratory. However, after some encouraging words from her mother, input from key mentors at the University of Texas Rio Grande Valley, or UTRGV, and a lot of hard work, Rodriguez landed at DOE’s Manufacturing Demonstration Facility, or MDF, at Oak Ridge National Laboratory.
The contract will be awarded to develop the newest high-performance computing system at the Oak Ridge Leadership Computing Facility.
The Department of Energy’s Oak Ridge National Laboratory has publicly released a new set of additive manufacturing data that industry and researchers can use to evaluate and improve the quality of 3D-printed components. The breadth of the datasets can significantly boost efforts to verify the quality of additively manufactured parts using only information gathered during printing, without requiring expensive and time-consuming post-production analysis.
SCALE users from 85 organizations across 21 countries gathered online and in person at Oak Ridge National Laboratory from June 5 to June 7 for the Eighth Annual SCALE Users Group Workshop. The meeting included 32 presentations and 14 hands-on tutorials on impactful and innovative applications of SCALE.
Participants in the SM2ART Research Experience for Undergraduates program got the chance to see what life is like in a research setting. REU participant Brianna Greer studied banana fibers as a reinforcing material in making lightweight parts for cars and bicycles.
Advanced materials research to enable energy-efficient, cost-competitive and environmentally friendly technologies for the United States and Japan is the goal of a memorandum of understanding, or MOU, between the Department of Energy’s Oak Ridge National Laboratory and Japan’s National Institute of Materials Science.
Andrew Conant from ORNL's nuclear nonproliferation division is collaborating with national laboratories to analyze isotopes generated in nuclear reactors. This research aims to glean insights into the operations and objectives of these reactors. ORNL, renowned for its leadership in nuclear research, maintains its legacy by promoting the peaceful utilization of nuclear energy worldwide.
Researchers at ORNL have developed the first additive manufacturing slicing computer application to simultaneously speed and simplify digital conversion of accurate, large-format three-dimensional parts in a factory production setting.