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Media Contacts
![Thomaz Carvalhaes. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-09/2022-P06032_0.jpg?h=252f27fa&itok=A2HrJ-y6)
In human security research, Thomaz Carvalhaes says, there are typically two perspectives: technocentric and human centric. Rather than pick just one for his work, Carvalhaes uses data from both perspectives to understand how technology impacts the lives of people.
![ORNL identity science researcher Nell Barber works on a facial recognition camera. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-07/Picture1.jpg?h=47322e82&itok=CTajZTiF)
Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
![Logan Sturm, Alvin M. Weinberg Fellow at ORNL, creates a mashup between additive manufacturing and cybersecurity research. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-05/sturm-lab.jpg?h=1de2f7a8&itok=nYiuVTGx)
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
![ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/2022-G00330_KESER%20Illustration_0.jpg?h=1cb48fc4&itok=c6ZuDdDg)
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
![Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.](/sites/default/files/styles/list_page_thumbnail/public/2022-04/CCSD_NeuralNetworkBanner.png?h=b16f811b&itok=fxqDEvs_)
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
![ORNL research scientist Christa Brelsford explained a mathematical framework she developed in 2018, which showed increased availability of infrastructure didn’t necessarily reduce inequality in its access. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-04/image001.jpg?h=1579b22a&itok=XzIna_Uc)
Unequal access to modern infrastructure is a feature of growing cities, according to a study published this week in the Proceedings of the National Academy of Sciences
![ORNL scientists created a new microbial trait mapping process that improves on classical protoplast fusion techniques to identify the genes that trigger desirable genetic traits like improved biomass processing. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy. Reprinted with the permission of Oxford University Press, publisher of Nucleic Acids Research](/sites/default/files/styles/list_page_thumbnail/public/2022-04/Nucleic%20Cover%20Illustration.jpg?h=4a9d1e17&itok=iw81emAt)
ORNL scientists had a problem mapping the genomes of bacteria to better understand the origins of their physical traits and improve their function for bioenergy production.
![This image illustrates lattice distortion, strain, and ion distribution in metal halide perovskites, which can be induced by external stimuli such as light and heat. Image credit: Stephen Jesse/ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-03/FerroicHalidePerovskite.jpg?h=b803af89&itok=eBzxpb4b)
A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.
![QLAN submit - A team from the U.S. Department of Energy’s Oak Ridge National Laboratory, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL using entangled photons passing through optical fiber. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/QLAN%20submit_0.jpg?h=cd715a88&itok=JV1MjQHH)
A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.
![This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy](/sites/default/files/styles/list_page_thumbnail/public/2022-01/thermosipho_collabfold2_0.jpg?h=3432ff3c&itok=4xhLbjKZ)
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.