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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 image illustrates lattice distortion, strain, and ion distribution in metal halide perovskites, which can be induced by external stimuli such as light and heat. Image credit: Stephen Jesse/ORNL

ORNL multimodal study sheds new light on promising photovoltaic material

A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.

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.

ORNL used novel additive manufacturing techniques to 3D print channel fasteners for Framatome’s boiling water reactor fuel assembly. Four components, like the one shown here, were installed at the TVA Browns Ferry nuclear plant. Credit: Framatome

Nuclear reactor components 3D printed by ORNL now installed at TVA Browns Ferry nuclear plant

Four first-of-a-kind 3D-printed fuel assembly brackets, produced at the Department of Energy’s Manufacturing Demonstration Facility at Oak Ridge National Laboratory, have been installed and are now under routine operating

ORNL researchers demonstrated a 3D printed power pole made of bioderived and recycled materials could be easily manufactured, transported and assembled, enabling the quick restoration of power after natural disasters. Credit: ORNL, U.S. Dept. of Energy

Manufacturing – Powered by nature

A team of researchers at Oak Ridge National Laboratory demonstrated the ability to additively manufacture power poles from bioderived and recycled materials, which could more quickly restore electricity after natural disasters.

ORNL researchers installed and demonstrated their wireless charging technology for the first time on an autonomous vehicle – the Local Motors Olli shuttle bus. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Wireless charging – Get on the bus

Oak Ridge National Laboratory researchers demonstrated their wireless charging technology on an autonomous electric vehicle for the first time in a project with Local Motors.

ORNL’s Sergei Kalinin and Rama Vasudevan (foreground) use scanning probe microscopy to study bulk ferroelectricity and surface electrochemistry -- and generate a lot of data. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

ORNL’s superb materials expertise, data and AI tools propel progress

At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.

Heavy-duty vehicles contribute 23% of transportation emissions of greenhouse gases and account for almost one-quarter of the fuel consumed annually in the U.S. Credit: Chris Bair/Unsplash

Heavy-duty vehicles an ideal entry into hydrogen fuel cell use

Through a consortium of Department of Energy national laboratories, ORNL scientists are applying their expertise to provide solutions that enable the commercialization of emission-free hydrogen fuel cell technology for heavy-duty

ORNL researchers are developing a method to print low-cost, high-fidelity, customizable sensors for monitoring power grid equipment. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Sensors – Printing on the fly

A method developed at Oak Ridge National Laboratory to print high-fidelity, passive sensors for energy applications can reduce the cost of monitoring critical power grid assets.

Pella Marion

Hydropower – More than megawatts

A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.