![Debjani Singh](/sites/default/files/styles/featured_square_large/public/2024-08/2024-P12408.jpg?h=8f9cfe54&itok=bP5F6U4Y)
Filter News
Area of Research
- (-) Electricity and Smart Grid (1)
- (-) Neutron Science (17)
- (-) Supercomputing (54)
- Advanced Manufacturing (6)
- Biological Systems (2)
- Biology and Environment (67)
- Biology and Soft Matter (1)
- Building Technologies (2)
- Clean Energy (107)
- Computational Biology (1)
- Computational Engineering (1)
- Computer Science (8)
- Energy Sciences (1)
- Functional Materials for Energy (1)
- Fusion and Fission (10)
- Fusion Energy (2)
- Isotope Development and Production (1)
- Isotopes (4)
- Materials (71)
- Materials for Computing (12)
- National Security (19)
- Nuclear Science and Technology (6)
- Quantum information Science (1)
News Topics
- (-) Artificial Intelligence (39)
- (-) Bioenergy (13)
- (-) Chemical Sciences (6)
- (-) Polymers (3)
- (-) Space Exploration (5)
- (-) Sustainable Energy (11)
- 3-D Printing/Advanced Manufacturing (10)
- Advanced Reactors (2)
- Big Data (20)
- Biology (14)
- Biomedical (25)
- Biotechnology (2)
- Buildings (4)
- Clean Water (2)
- Climate Change (17)
- Composites (1)
- Computer Science (98)
- Coronavirus (17)
- Critical Materials (3)
- Cybersecurity (9)
- Decarbonization (7)
- Energy Storage (14)
- Environment (28)
- Exascale Computing (22)
- Fossil Energy (1)
- Frontier (29)
- Fusion (2)
- Grid (7)
- High-Performance Computing (39)
- Isotopes (1)
- Machine Learning (16)
- Materials (28)
- Materials Science (33)
- Mathematics (1)
- Microelectronics (1)
- Microscopy (8)
- Molten Salt (1)
- Nanotechnology (19)
- National Security (8)
- Net Zero (1)
- Neutron Science (101)
- Nuclear Energy (7)
- Partnerships (1)
- Physics (16)
- Quantum Computing (19)
- Quantum Science (29)
- Security (6)
- Simulation (14)
- Software (1)
- Summit (42)
- Transportation (10)
Media Contacts
![Molecular dynamics simulations of the Fs-peptide revealed the presence of at least eight distinct intermediate stages during the process of protein folding. The image depicts a fully folded helix (1), various transitional forms (2–8), and one misfolded state (9). By studying these protein folding pathways, scientists hope to identify underlying factors that affect human health.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/Slide1_0.png?h=c855054e&itok=aNbgxXsc)
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.
![(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.](/sites/default/files/styles/list_page_thumbnail/public/2019-03/TourassiWH%5B1%5D.png?h=26b5064d&itok=HUC2iYmE)
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
![Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing. Using as much as 50 percent lignin by weight, a new composite material created at ORNL is well suited for use in 3D printing.](/sites/default/files/styles/list_page_thumbnail/public/2018-P09551.jpg?itok=q7Ri01Qb)
Scientists at the Department of Energy’s Oak Ridge National Laboratory have created a recipe for a renewable 3D printing feedstock that could spur a profitable new use for an intractable biorefinery byproduct: lignin.
Scientists at Oak Ridge National Laboratory have conducted a series of breakthrough experimental and computational studies that cast doubt on a 40-year-old theory describing how polymers in plastic materials behave during processing.
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the