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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.

ORNL biogeochemist Teri O’Meara is focused on improving how coastal systems are represented in global climate models, enabling better predictions about the future of these critical ecosystems. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Teri O’Meara: Making a splash with coastal ecology

Surrounded by the mountains of landlocked Tennessee, Oak Ridge National Laboratory’s Teri O’Meara is focused on understanding the future of the vitally important ecosystems lining the nation’s coasts.

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. 

The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

Updated exascale system for Earth simulations delivers twice the speed

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

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.

U.S. Secretary of Energy Granholm tours ORNL’s world-class science facilities

U.S. Secretary of Energy Granholm tours ORNL’s world-class science facilities

Energy Secretary Jennifer Granholm visited ORNL on Nov. 22 for a two-hour tour, meeting top scientists and engineers as they highlighted projects and world-leading capabilities that address some of the country’s most complex research and technical challenges. 

Benjamin Sulman, a scientist in ORNL’s Environmental Sciences Division, creates Earth system models that simulate how plants, microbes and soils interact and influence the cycling of carbon, water and nutrients in their environment. His work aims to helps researchers across disciplines better understand complex, rapidly changing ecosystems, including coastal wetlands and Arctic permafrost soils. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Benjamin Sulman: Building better Earth system models

As rising global temperatures alter ecosystems worldwide, the need to accurately simulate complex environmental processes under evolving conditions is more urgent than ever.

Oak Ridge National Laboratory’s MENNDL AI software system can design thousands of neural networks in a matter of hours. One example uses a driving simulator to evaluate a network’s ability to perceive objects under various lighting conditions. Credit: ORNL, U.S. Dept. of Energy

ORNL licenses revolutionary AI system to General Motors for automotive use

The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.

Distinguished Inventors

Six ORNL scientists receive Distinguished Inventor honor

Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.