Filter News
Area of Research
- Advanced Manufacturing (22)
- Biology and Environment (32)
- Building Technologies (2)
- Clean Energy (101)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Computational Engineering (3)
- Computer Science (15)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (1)
- Fusion and Fission (27)
- Fusion Energy (15)
- Isotopes (1)
- Materials (45)
- Materials for Computing (10)
- Mathematics (1)
- National Security (22)
- Neutron Science (20)
- Nuclear Science and Technology (14)
- Quantum information Science (6)
- Supercomputing (117)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (128)
- (-) Computer Science (199)
- (-) Frontier (46)
- (-) Fusion (59)
- Advanced Reactors (35)
- Artificial Intelligence (102)
- Big Data (62)
- Bioenergy (92)
- Biology (102)
- Biomedical (62)
- Biotechnology (24)
- Buildings (67)
- Chemical Sciences (74)
- Clean Water (31)
- Climate Change (106)
- Composites (30)
- Coronavirus (46)
- Critical Materials (29)
- Cybersecurity (35)
- Decarbonization (85)
- Education (5)
- Element Discovery (1)
- Emergency (2)
- Energy Storage (112)
- Environment (201)
- Exascale Computing (44)
- Fossil Energy (6)
- Grid (67)
- High-Performance Computing (94)
- Hydropower (11)
- Irradiation (3)
- Isotopes (57)
- ITER (7)
- Machine Learning (51)
- Materials (150)
- Materials Science (149)
- Mathematics (10)
- Mercury (12)
- Microelectronics (4)
- Microscopy (51)
- Molten Salt (9)
- Nanotechnology (60)
- National Security (73)
- Net Zero (14)
- Neutron Science (140)
- Nuclear Energy (111)
- Partnerships (51)
- Physics (64)
- Polymers (33)
- Quantum Computing (39)
- Quantum Science (73)
- Renewable Energy (2)
- Security (26)
- Simulation (53)
- Software (1)
- Space Exploration (25)
- Statistics (3)
- Summit (61)
- Sustainable Energy (130)
- Transformational Challenge Reactor (7)
- Transportation (99)
Media Contacts
John joined the MPEX project in 2019 and has served as project manager for several organizations within ORNL.
The award was given in “recognition of his lifelong leadership in fusion technology for plasma fueling systems in magnetically confined fusion systems.”
Prasanna Balaprakash, director of AI programs at the Department of Energy’s Oak Ridge National Laboratory, has been appointed to Tennessee’s Artificial Intelligence Advisory Council.
The world’s fastest supercomputer helped researchers simulate synthesizing a material harder and tougher than a diamond — or any other substance on Earth. The study used Frontier to predict the likeliest strategy to synthesize such a material, thought to exist so far only within the interiors of giant exoplanets, or planets beyond our solar system.
Two additive manufacturing researchers from ORNL received prestigious awards from national organizations. Amy Elliott and Nadim Hmeidat, who both work in the Manufacturing Science Division, were recognized recently for their early career accomplishments.
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.
Power companies and electric grid developers turn to simulation tools as they attempt to understand how modern equipment will be affected by rapidly unfolding events in a complex 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.