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ORNL researcher Louise Evans is working to ensure safeguards approaches and verification technologies are integrated early in the design process of advanced reactor technologies. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy
Decades of nuclear expertise guides global nonproliferation innovation
To turn carbon dioxide, or CO2, into methanol, or CH3OH, copper (shown in yellow) on a hydride-substituted support speeds reactions mediated by hydrides and catalyzed by hydrogen atoms (shown in black) from surface-adsorbed formate, HCOO*. Credit: Yang He/ORNL, U.S. Dept. of Energy
Ion swap improves CO2-defeating catalyst
Caption: The Na-CO2 battery developed at ORNL, consisting of two electrodes in a saltwater solution, pulls atmospheric carbon dioxide into its electrochemical reaction, and releases only valuable biproducts. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy
Carbon-capture batteries store renewable energy

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ORNL researchers led by Michael Garvin, left, and David Kainer discovered genetic mutations called structural variants and linked them to autism spectrum disorders, demonstrating an approach that could be used to develop better diagnostics and drug therapies. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Genetic markers for autism, hiding in plain sight

ORNL researchers discovered genetic mutations that underlie autism using a new approach that could lead to better diagnostics and drug therapies.

The AI-driven HyperCT platform has three primary points of articulation that can rotate a sample in almost any direction, eliminating the need for human intervention and significantly reducing lengthy experiment times. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy

Automating neutron experiments with AI

Oak Ridge National Laboratory researchers are developing a first-of-its-kind artificial intelligence device for neutron scattering called Hyperspectral Computed Tomography, or HyperCT.

Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions. Credit: Getty Images

Climate — Improving model accuracy

Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.

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

Public release of ORNL global population distribution data aids humanitarian support

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

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.

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.