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Marthi receives IEEE senior member honor
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Keeping aging structures safe
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Building energy around changing climate

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Oak Ridge National Laboratory researchers used big area additive manufacturing with metal to 3D print a steel component for a wind turbine, proving the technique as a viable alternative to conventional fabrication methods. Credit: ORNL, U.S. Dept. of Energy

Manufacturing — Printing in the wind

Oak Ridge National Laboratory researchers recently used large-scale additive manufacturing with metal to produce a full-strength steel component for a wind turbine, proving the technique as a viable alternative to

A new process developed by Oak Ridge National Laboratory leverages deep learning techniques to study cell movements in a simulated environment, guided by simple physics rules similar to video-game play. Credit: MSKCC and UTK

AI — Revealing hidden biology

Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity. 

ORNL has developed the SolidPAC tool to help researchers design energy-dense, long-lived and safe solid-state batteries. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Energy storage — Calculating better batteries

Scientists can speed the design of energy-dense solid-state batteries using a new tool created by Oak Ridge National Laboratory.

Ultra Safe Nuclear Corporation has licensed a novel method to 3D print highly resistant components for use in nuclear reactor designs. USNC Executive Vice President Kurt Terrani, formerly of ORNL, said the novel method will allow the company to make parts with desired complex shapes more efficiently. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Ultra Safe Nuclear licenses ORNL method to 3D print advanced reactor components

A novel method to 3D print components for nuclear reactors, developed by the Department of Energy’s Oak Ridge National Laboratory, has been licensed by Ultra Safe Nuclear Corporation.

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.