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Oak Ridge National Laboratory’s MENNDL AI software system can design thousands of neural networks in a matter of hours. One example uses a driving simulator to evaluate a network’s ability to perceive objects under various lighting conditions. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.

Jianlin Li, leader of the Energy Storage and Conversion Manufacturing Group, directs the development of advanced manufacturing schemes and pilot-scale devices into emerging energy storage and conversion research. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

In his career focused on energy storage science, Jianlin Li has learned that discovering new ways to process and assemble batteries is just as important as the development of new materials.

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

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

Nuclear engineer Nesrin Ozgan Cetiner led ORNL’s collaboration with AMS Corp. to test instrument and control sensors for the next generation of nuclear power reactor technology. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Toward the goal of bringing the next generation of nuclear power reactor technology online this decade, ORNL and Analysis and Measurement Services Corporation have successfully completed loop testing of instrument and control sensors for an advanced reactor design for small modular reactors.

The proposed Battery Identity Global Passport suggests a scannable QR code or other digital tag affixed to Li-ion batteries to identify materials for efficient end-of-life recycling. Credit: Andy Sproles, ORNL/U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory have devised a method to identify the unique chemical makeup of every lithium-ion battery around the world, information that could accelerate recycling, recover critical materials and resolve a growing waste stream.

Spin chains in a quantum system undergo a collective twisting motion as the result of quasiparticles clustering together. Demonstrating this KPZ dynamics concept are pairs of neighboring spins, shown in red, pointing upward in contrast to their peers, in blue, which alternate directions. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy

Using complementary computing calculations and neutron scattering techniques, researchers from the Department of Energy’s Oak Ridge and Lawrence Berkeley national laboratories and the University of California, Berkeley, discovered the existence of an elusive type of spin dynamics in a quantum mechanical system.

ORNL researchers used an electrochemical process to heal dendrites that formed in a ceramic, garnet-based catalyst designed for a solid-state lithium battery. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory successfully demonstrated a technique to heal dendrites that formed in a solid electrolyte, resolving an issue that can hamper the performance of high energy-density, solid-state batteries.

Researchers at ORNL and the University of Tennessee developed an automated workflow that combines chemical robotics and machine learning to speed the search for stable perovskites. Credit: Jaimee Janiga/ORNL, U.S. Dept of Energy

Researchers at the Department of Energy’s Oak Ridge National Laboratory and the University of Tennessee are automating the search for new materials to advance solar energy technologies.

Xin Sun

Xin Sun has been selected as the associate laboratory director for the Energy Science and Technology Directorate, or ESTD, at the Department of Energy’s Oak Ridge National Laboratory.

Each point on the sphere of this visual representation of arbitrary frequency-bin qubit states corresponds to a unique quantum state, and the gray sections represent the measurement results. The zoomed-in view illustrates examples of three quantum states plotted next to their ideal targets (blue dots). Credit: Joseph Lukens/ORNL, U.S. Dept. of Energy

A team of researchers at Oak Ridge National Laboratory and Purdue University has taken an important step toward this goal by harnessing the frequency, or color, of light. Such capabilities could contribute to more practical and large-scale quantum networks exponentially more powerful and secure than the classical networks we have today.