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ORNL's Communications team works with news media seeking information about the laboratory. Media may use the resources listed below or send questions to news@ornl.gov.

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A smart approach to microscopy and imaging developed at Oak Ridge National Laboratory could drive discoveries in materials for future technologies. Credit: Adam Malin/ORNL, U.S. Dept. of Energy

Self-driving microscopes discover shortcuts to new materials

Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.

With seismic and acoustic data recorded by remote sensors near ORNL’s High Flux Isotope Reactor, researchers could predict whether the reactor was on or off with 98% accuracy. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

Seismic — Feeling it out

An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.

Oak Ridge National Laboratory researchers built an Earth-to-space communications system to work with private and government partners with the goal of directly connecting data downlinks to high performance computing. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Communications — Space to ground

Oak Ridge National Laboratory is debuting a small satellite ground station that uses high-performance computing to support automated detection of changes to Earth’s landscape.

Oak Ridge National Laboratory researchers quantified human behaviors during the early days of COVID-19, which could be useful for disaster response or city planning. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

COVID — Changing behavior

Researchers at Oak Ridge National Laboratory have empirically quantified the shifts in routine daytime activities, such as getting a morning coffee or takeaway dinner, following safer at home orders during the early days of the COVID-19 pandemic.

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

VA, ORNL and Harvard develop novel method to identify complex medical relationships

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.

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

Retro technique advances modern bacterial engineering for bioenergy

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.

Dongarra in 2019 with Oak Ridge National Laboratory's Summit supercomputer

Dongarra named Turing Award recipient for advances in high-performance computing, AI

A force within the supercomputing community, Jack Dongarra developed software packages that became standard in the industry, allowing high-performance computers to become increasingly more powerful in recent decades.

A new process developed by Oak Ridge National Laboratory leverages deep learning techniques to study cell movements in a simulated environment, guided by simple physics rules similar to video-game play. Credit: MSKCC and UTK

AI — Revealing hidden biology

Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity. 

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

Scientists use Summit supercomputer, deep learning to predict protein functions at genome scale

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.

ORNL is making underused or inaccessible bioenergy data available to accelerate 鶹Ӱ for the bioeconomy. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Bioenergy — Data boost

A research team from Oak Ridge National Laboratory has identified and improved the usability of data that can help accelerate 鶹Ӱ for the growing bioeconomy.