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The OpeN-AM experimental platform, installed at the VULCAN instrument at ORNL’s Spallation Neutron Source, features a robotic arm that prints layers of molten metal to create complex shapes. This allows scientists to study 3D printed welds microscopically. Credit: Jill Hemman, ORNL/U.S. Dept. of Energy

Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.

Director of ORNL’s AI Initiative Prasanna Balaprakash addresses attendees at the Generative AI for ORNL Science Workshop. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory hosted its Smoky Mountains Computational Science and Engineering Conference for the first time in person since the COVID pandemic broke in 2020. The conference, which celebrated its 20th consecutive year, took place at the Crowne Plaza Hotel in downtown Knoxville, Tenn., in late August.

An AI-generated image representing atoms and artificial neural networks. Credit: Maxim Ziatdinov, ORNL

Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.

COHERENT collaborators were the first to observe coherent elastic neutrino–nucleus scattering. Their results, published in the journal Science, confirm a prediction of the Standard Model and establish constraints on alternative theoretical models. Image c

After more than a year of operation at the Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL), the COHERENT experiment, using the world’s smallest neutrino detector, has found a big fingerprint of the elusive, electrically neutral particles that interact only weakly with matter.

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Virginia-based Lenvio Inc. has exclusively licensed a cyber security technology from the Department of Energy’s Oak Ridge National Laboratory that can quickly detect malicious behavior in software not previously identified as a threat.