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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.

Earth Day

ORNL brings big science to address the climate challenge

Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time. 

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

Study measuring infrastructure inequity addresses disparities in growing cities

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

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.

Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals. Credit: ORNL, U.S. Dept. of Energy

Three ORNL scientists elected AAAS fellows

Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.

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.

ORNL Corporate Research Fellow and Geospatial Science and Human Security Division Director Budhu Bhaduri has been elected as a fellow of the American Association of Geographers. The honor recognizes Bhaduri for his “innovation, mentorship and wide-ranging leadership” in geographic sciences. Credit: ORNL, U.S. Dept. of Energy

Budhu Bhaduri elected fellow of the American Association of Geographers

ORNL’s Budhendra “Budhu” Bhaduri has been elected a fellow of the American Association of Geographers. The honor recognizes Bhaduri as “a world leader in innovation, development and application of research in human dynamics, geographic data science, remote sensing and scalable geocomputation.”

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.