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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.

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

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.

The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

Using quantum Monte Carlo methods, the researchers simulated bulk VO2. Yellow and turquoise represent changes in electron density between the excited and ground states of a compound composed of oxygen, in red, and vanadium, in blue, which allowed them to evaluate how an oxygen vacancy, in white, can alter the compound’s properties. Credit: Panchapakesan Ganesh/ORNL, U.S. Dept. of Energy

Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant

Burak Ozpineci is a globally recognized leader in power electronics research. He was named an ORNL Corporate Fellow in fall 2021. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Burak Ozpineci started out at ORNL working on a novel project: introducing silicon carbide into power electronics for more efficient electric vehicles. Twenty years later, the car he drives contains those same components.

A material’s spins, depicted as red spheres, are probed by scattered neutrons. Applying an entanglement witness, such as the QFI calculation pictured, causes the neutrons to form a kind of quantum gauge. This gauge allows the researchers to distinguish between classical and quantum spin fluctuations. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

A team led by the U.S. Department of Energy’s Oak Ridge National Laboratory demonstrated the viability of a “quantum entanglement witness” capable of proving the presence of entanglement between magnetic particles, or spins, in a quantum material.

A traffic-camera view of Shallowford Road, one of the more than 350 intersections in Chattanooga studied by Oak Ridge National Laboratory researchers.

The daily traffic congestion along the streets and interstate lanes of Chattanooga could be headed the way of the horse and buggy with help from ORNL researchers.

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

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

L-R: ORNL’s Omer Onar and Veda Galigekere with the dynamic wireless charging test bed at ORNL’s Grid Research Integration and Deployment Center. Credit: Carlos Jones, ORNL/U.S. Dept. of Energy

Consumer buy-in is key to the future of a decarbonized transportation sector in which electric vehicles largely replace today’s conventionally fueled cars and trucks.

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

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.