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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.
![Eugene Dumitrescu, Ben Lawrie, Matthew Feldman, and Jordan Hachtel (from left) have conducted investigations aimed at controlling the dissipative nature of quantum systems and materials. The cathodoluminescence microscope used in their work appears at rig Eugene Dumitrescu, Ben Lawrie, Matthew Feldman, and Jordan Hachtel (from left) have conducted investigations aimed at controlling the dissipative nature of quantum systems and materials. The cathodoluminescence microscope used in their work appears at rig](/sites/default/files/styles/list_page_thumbnail/public/Quantum%20physics%20main%20photo%5B1%5D_0.jpg?itok=Y67Yqnmc)
![Small modular reactor computer simulation](/sites/default/files/styles/list_page_thumbnail/public/2019-04/Nuclear_simulation_scale-up.jpg?h=5992a83f&itok=A0oscIPL)
Nuclear scientists at Oak Ridge National Laboratory are retooling existing software used to simulate radiation transport in small modular reactors, or SMRs, to run more efficiently on next-generation supercomputers. ORNL is working on various aspects of advanced SMR designs through s...
![Joseph Lukens, Pavel Lougovski and Nicholas Peters (from left), researchers with ORNL’s Quantum Information Science Group, are examining methods for encoding photons with quantum information that are compatible with the existing telecommunications infrast Joseph Lukens, Pavel Lougovski and Nicholas Peters (from left), researchers with ORNL’s Quantum Information Science Group, are examining methods for encoding photons with quantum information that are compatible with the existing telecommunications infrast](/sites/default/files/styles/list_page_thumbnail/public/news/images/QIS%20photo%5B1%5D.jpg?itok=CPhznRBf)
![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
![An example of a spiking neural network shows how data can be classified using the neuromorphic device. Credit: Catherine Schuman and Margaret Drouhard/Oak Ridge National Laboratory, U.S. Dept. of Energy An example of a spiking neural network shows how data can be classified using the neuromorphic device. Credit: Catherine Schuman and Margaret Drouhard/Oak Ridge National Laboratory, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/Spiking_neural_network_ORNL_2.jpg?itok=CN68Ze_4)