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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.

An international team of researchers used Summit to model spin, charge and pair-density waves in cuprates, a type of copper alloy, to explore the materials’ superconducting properties. The results revealed new insights into the relationships between these dynamics as superconductivity develops. Credit: Jason Smith/ORNL

Summit study spins up new insights into correlated electron systems

A study led by researchers at ORNL used the nation’s fastest supercomputer to close in on the answer to a central question of modern physics that could help conduct development of the next generation of energy technologies.

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’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, Oak Ridge National Laboratory researchers studied piezoelectric materials, which convert mechanical stress into electrical energy, to see how they could handle bombardment with energetic neutrons.

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.

Nesaraja split her effort between nuclear data evaluation and experimentation at ORNL’s now-closed Holifield Radioactive Ion Beam Facility. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Caroline Nesaraja: Providing nothing but the best nuclear data

Nuclear physicist Caroline Nesaraja of the Department of Energy’s Oak Ridge National Laboratory evaluates nuclear data vital to applied and basic sciences. 

SCGSR Awardee Jacob Zettlemoyer, Indiana University Bloomington, led data analysis and worked with ORNL’s Mike Febbraro on coatings, shown under blue light, to shift argon light to visible wavelengths to boost detection. Credit: Rex Tayloe/Indiana University

Compelling evidence of neutrino process opens physics possibilities

The COHERENT particle physics experiment at the Department of Energy’s Oak Ridge National Laboratory has firmly established the existence of a new kind of neutrino interaction.

The researchers embedded a programmable model into a D-Wave quantum computer chip. Credit: D-Wave

Supercomputers aid scientists studying the smallest particles in the universe

Since the 1930s, scientists have been using particle accelerators to gain insights into the structure of matter and the laws of physics that govern our world.