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![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.
![To develop complex materials with superior properties, Vera Bocharova uses diverse methods including broadband dielectric spectroscopy. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Jason Richards](/sites/default/files/styles/list_page_thumbnail/public/2019-02/2016-p05202.jpg?h=b6236d98&itok=w-Sd8giq)
Vera Bocharova at the Department of Energy’s Oak Ridge National Laboratory investigates the structure and dynamics of soft materials—polymer nanocomposites, polymer electrolytes and biological macromolecules—to advance materials and technologies for energy, medicine and other applications.
![ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La](/sites/default/files/styles/list_page_thumbnail/public/study_area_one_dest_2.jpg?itok=2cWFkQvW)
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.
![From left, Amit Naskar, Ngoc Nguyen and Christopher Bowland in ORNL’s Carbon and Composites Group bring a new capability—structural health monitoring—to strong, lightweight materials promising for transportation applications. From left, Amit Naskar, Ngoc Nguyen and Christopher Bowland in ORNL’s Carbon and Composites Group bring a new capability—structural health monitoring—to strong, lightweight materials promising for transportation applications.](/sites/default/files/styles/list_page_thumbnail/public/IMAGE1_%202018-P06604_0.jpg?itok=9-iSLuHf)
Carbon fiber composites—lightweight and strong—are great structural materials for automobiles, aircraft and other transportation vehicles. They consist of a polymer matrix, such as epoxy, into which reinforcing carbon fibers have been embedded. Because of differences in the mecha...
![Rose Ruther and Jagjit Nanda have been collaborating to develop a membrane for a low-cost redox flow battery for grid-scale energy storage. Rose Ruther and Jagjit Nanda have been collaborating to develop a membrane for a low-cost redox flow battery for grid-scale energy storage.](/sites/default/files/styles/list_page_thumbnail/public/MembraneRoseJagjitFilterSmile.jpg?itok=p8-Q46wn)
Oak Ridge National Laboratory scientists have developed a crucial component for a new kind of low-cost stationary battery system utilizing common materials and designed for grid-scale electricity storage. Large, economical electricity storage systems can benefit the nation’s grid ...
![An Oak Ridge National Laboratory–led team has developed super-stretchy polymers with amazing self-healing abilities that could lead to longer-lasting consumer products. An Oak Ridge National Laboratory–led team has developed super-stretchy polymers with amazing self-healing abilities that could lead to longer-lasting consumer products.](/sites/default/files/styles/list_page_thumbnail/public/Super-stretchy-self-healing-material.png?itok=jgiEGKgS)
An Oak Ridge National Laboratory–led team has developed super-stretchy polymers with amazing self-healing abilities that could lead to longer-lasting consumer products.
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.
![Composites scientist and engineer Vlastimil Kunc with the latest large-scale 3Dprinter at the MDF. Composites scientist and engineer Vlastimil Kunc with the latest large-scale 3Dprinter at the MDF.](/sites/default/files/styles/list_page_thumbnail/public/news/images/Kunc200.jpg?itok=Z4xuRYuR)
Vlastimil Kunc grew up in a family of scientists where his natural curiosity was encouraged—an experience that continues to drive his research today in polymer composite additive manufacturing at Oak Ridge National Laboratory. “I’ve been interested in the science of composites si...
![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