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It’s been referenced in Popular Science and Newsweek, cited in the Economic Report of the President, and used by agencies to create countless federal regulations.

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

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.

Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.

Earth Day

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

Andrew Sutton joined ORNL in 2020 to guide a newly formed team that focuses on chemical process scale-up in advanced manufacturing. Credit: ORNL, U.S. Dept. of Energy

When Andrew Sutton arrived at ORNL in late 2020, he knew the move would be significant in more ways than just a change in location.

David McCollum is bringing his interdisciplinary expertise in engineering, economics and policy to several initiatives at Oak Ridge National Laboratory in the global effort to transform energy systems equitably while respecting planetary boundaries. Credit: Lindsay McCollum

David McCollum is using his interdisciplinary expertise, international networks and boundless enthusiasm to lead Oak Ridge National Laboratory’s contributions to the Net Zero World initiative.

A zoomed in view of downtown Chattanooga’s sensors, which allowed the researchers to create building occupancy schedules that could enable improved energy efficiency and faster emergency responses. Credit: Andy Berres/ORNL, U.S. Dept. of Energy

Every day, hundreds of thousands of commuters across the country travel from houses, apartments and other residential spaces to commercial buildings — from offices and schools to gyms and grocery stores.

Bruce Warmack is using his physics and electrical engineering expertise to analyze advanced sensors for the power grid on a new testbed he developed at the Distributed Energy Communications and Controls Laboratory at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Bruce Warmack has been fascinated by science since his mother finally let him have a chemistry set at the age of nine. He’d been pestering her for one since he was six.

Brian Fricke, equipment research group lead at ORNL, works with Anthony Gehl at the Building Technologies Research and Integration Center on a new prototype installation. Collaboration with fellow researchers across the building technologies area strengthens his group’s capabilities. Credit: ORNL, U.S. Dept. of Energy

When Brian Fricke walks into a supermarket, evidence of his scientific achievement is all around in the refrigerated cases housing the fresh fruits and vegetables. As an Oak Ridge National Laboratory building equipment researcher, Fricke has a long history of making sure that produce is kept fresh in an energy efficient and environmentally sound manner.

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

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.