Science and Technology Highlights

Models developed by a team of authors at LLNL explain the unusual behavior of plutonium. From left to right, Lorin Benedict, Alex Landa, Kyoung Eun Kweon, Emily Moore, Per Söderlind, Christine Wu, Nir Goldman, Randy Hood and Aurelien Perron. Not pictured are Babak Sadigh and Lin Yang.
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In a new study, LLNL researchers demonstrate a model that can reproduce and explain delta-plutonium’s thermal behavior and unusual properties. 

Researchers at Lawrence Livermore National Laboratory have reached a milestone in combining AI with fusion target design by deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments.
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LLNL researchers have reached a milestone in combining AI with fusion target design by deploying AI agents to automate and accelerate inertial confinement fusion (ICF) experiments.

An artist rendering of two water droplets playing a game of tic-tac-toe.
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LLNL researchers create a droplet-based platform that uses ions to perform simple neuromorphic computations.

The 3D quantum ghost imaging microscope setup. A laser and crystal (left) are used to make entangled photons, which are split and sent in two directions. One turns left to hit and scatter off a sample, providing a standard image at a 90-degree angle. The other continues straight and is used to construct a ghost image.
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LLNL scientists develop a 3D quantum ghost imaging microscope — the first of its kind.

Shown is a rendering of Firefly’s Elytra Dawn vehicle utilizing LLNL’s telescope to perform space domain awareness operations.
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LLNL is selected to provide a new monolithic telescope for a responsive space mission that will launch as early as 2027.

Under the three-year DeNOVO project, Lawrence Livermore National Laboratory and other institutions will apply high-performance computing and AI to push the boundaries of antibody design.
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Under the three-year DeNOVO project, LLNL and other institutions will apply high-performance computing and AI to push the boundaries of antibody design. 

On Sept. 11, 2001, the collapse of the World Trade Center in New York City released a toxic plume, as seen in this photo taken from aboard the International Space Station.
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In a study published in PNAS Nexus, LLNL researchers described how a new deep learning model is capable of predicting toxic plume behavior in just a few minutes. 

The Autonomous Alloy Prediction and EXperimentation (APEX) platform aims to accelerate the alloy-discovery process by leveraging robotics and machine learning to design, build and test samples without human intervention. LLNL robotics and materials engineering intern Andre Fatehi monitors the APEX platform during an experimental test run.
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LLNL is partnering with Cornell University to build the Autonomous Alloy Prediction and EXperimentation (APEX) platform for 3D printing, grinding, polishing and characterizing alloy samples.

From left: chemists Brian Mayer and Katelyn Mason and biologist Todd Corzett observe the operation of the robot that independently executes the acetylchlolinersterase assays the team uses to assess Novichok inhibition and to discover new oxime antidotes for Novichok poisoning.
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LLNL scientists and Purdue University collaborators develop and demonstrate a high-throughput, automated mass spectrometry platform.

LLNL and ELI began their partnership with the L3 HAPLS laser system, which LLNL built and delivered, and ELI now operates.
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LLNL and the Extreme Light Infrastructure (ELI) European Research Infrastructure Consortium (ERIC) have signed a new Memorandum of Understanding that builds on their existing strategic collaboration for scientific research and laser innovation.