News

Photo of glowing, pink diamond-shaped figure. This is illuminated with light, encircled with a wreath of around 70 blue tube-like shapes.
Promethium’s secrets exposed
Male student wearing clear safety goggles peering through a 3D-printed item.
tnAchieves students learn about lab careers
Photo of a man in a black jacket suit with a blue shirt crossing his arms outside. He is smiling.
Wagner receives ASME Medal

Filter News

Area of Research

News Type

Date

News Topics

Media Contacts

Connect with ORNL

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.

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.

ORNL neural network study harnesses made-to-order design to pair properties to materials

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

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

Traffic-based analyses of buildings advance smart city capabilities

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.

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.