Science and Technology Highlights

Scientists at Lawrence Livermore National Laboratory (LLNL) and their collaborators have created a new class of programmable soft materials that can absorb impacts like never before, while also changing shape when heated.
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LLNL team develops new material that bends, bounces and absorbs energy on demand

LLNL scientists and collaborators have created a new class of programmable soft materials that can absorb impacts like never before.

A reflection of Brian Bauman (left), the space hardware principal optical engineer and inventor of the monolithic telescope and Frank Ravizza, the space hardware optical engineering lead, is seen on the primary mirror surface on a flight-ready 175-millimeter aperture monolithic telescope. Additionally, Ravizza is seen holding a 25-millimeter aperture monolithic optic. The ease of handling showcases the robust design incorporated in all monolithic telescopes.
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LLNL and Starris sign agreement, schedule conference talk for Aug. 13

Optimax Space Systems have signed a Cooperative Research and Development Agreement (CRADA), expanding production of LLNL’s next-generation space domain awareness technology. 

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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LLNL demonstrates new model that explains plutonium’s peculiar behavior

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 pushes frontier of fusion target design with AI

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 makes advances in neuromorphic computing

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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First-of-its-kind microscope takes 3D ghost images of nanoparticles

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 selected by Defense Innovation Unit to build Pathfinder telescope for spac…

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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Lawrence Livermore National Laboratory leads groundbreaking DeNOVO initiative i…

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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Deep-learning model predicts how toxic plumes move through cities

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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Self-driving lab to automate the discovery of novel alloys

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.