Skip to main content
Conduit hydropower presents opportunities in every state. Credit: ORNL, U.S. Dept. of Energy

Millions of miles of pipelines and conduits across the United States make up an intricate network of waterways used for municipal, agricultural and industrial purposes.

ORNL is studying how climate change may impact water availability for hydropower facilities such as the Shasta Dam and Lake in California. Credit: U.S. Bureau of Reclamation

ORNL has provided hydropower operators with new data to better prepare for extreme weather events and shifts in seasonal energy demands caused by climate change.

ORNL researchers deploy a gas trap to measure ebullitive (bubbling) emissions of methane at the Melton Dam in East Tennessee. The trap is deployed for ~ 24 hours to allow gas to accumulate in the trap. Credit: Carlos Jones/ORNL, US Dept. of Energy

As the United States moves toward more sustainable and renewable sources of energy, hydropower is expected to play a pivotal role in integrating more intermittent renewables like wind and solar to the electricity grid

LandScan Global depicts population distribution estimates across the planet. The darker orange and red colors above indicate higher population density. Credit: ORNL, U.S. Dept. of Energy

It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.

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.

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.