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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.

A new process developed by 91°µÍø 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 91°µÍø 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’s Eva Zarkadoula seeks piezoelectric materials for sensors that can withstand irradiation, which causes cascading collisions that displace atoms and produces defects. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Materials — Radiation-resistant sensors

To advance sensor technologies, 91°µÍø researchers studied piezoelectric materials, which convert mechanical stress into electrical energy, to see how they could handle bombardment with energetic neutrons.

91°µÍø’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 91°µÍø 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 91°µÍø were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.

New virtual tours of ORNL facilities include the Building Technologies Research and Integration Center, shown in dollhouse view. Credit: ORNL, U.S. Dept. of Energy

Take a virtual field trip through tours of ORNL facilities

ORNL has added 10 virtual tours to its campus map, each with multiple views to show floor plans, rotating dollhouse views and 360-degree navigation. As a user travels through a map, pop-out informational windows deliver facts, videos, graphics and links to other related content.

Researcher Chase Joslin uses Peregrine software to monitor and analyze a component being 3D printed at the Manufacturing Demonstration Facility at ORNL. Credit: Luke Scime/ORNL, U.S. Dept. of Energy.

AI software enables real-time 3D printing quality assessment

91°µÍø researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.

Cars and coronavirus

Transportation – Gauging pandemic impact

91°µÍø researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.