Science and Technology Highlights

Project DarkStar leverages artificial intelligence and machine learning to optimize shaped charges—explosive devices used to manipulate metals.
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LLNL researchers couple computing capabilities and manufacturing methods to rapidly develop and experimentally validate modifications to a shaped charge.

Artwork illustrating a new study combining atomistic simulations, machine learning potential, and data-driven methods to study the chemical speciation of amorphous carbon nitride using X-ray absorption near-edge structure (XANES) spectra.
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LLNL scientists develop a new approach that can rapidly predict the structure and chemical composition of heterogeneous materials.

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving Lawrence Livermore National Laboratory researchers has successfully combined an artificial intelligence-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromised by viral evolution. The work was published in the journal Nature.
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A multi-institutional team involving LLNL researchers successfully combines an artificial intelligence (AI)-backed platform with supercomputing to redesign and restore antibody effectiveness. 

A 2D MARBL simulation of the N210808 “Burning Plasma” shot performed at the National Ignition Facility at the onset of ignition. This calculation consists of 19 million high-order quadrature points and ran on rzAdams (on AMD MI300A GPUs).
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Researchers at LLNL accelerate and add features to complex multi-physics simulations run on Graphics Processing Units (GPUs), a development that could advance high performance computing and engineering.

Opportunistic pathogenic species, such as Acinetobacter, are prevalent in combat wound infections and commonly found on the gear of U.S. military service members.
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To support the early detection of potentially detrimental microbial factors, LLNL researchers have developed a targeted panel for the capture and sequencing of microbial genomic signatures.

During exploration drilling at the Halleck Creek Rare Earth project, geologists conduct field surface research.
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Using a bioengineered protein-based technology, LLNL scientists and collaborators develop a new separation technique for rare-earth elements (REE).

LLNL researchers have have found  that soils, a huge carbon pool, tend to lose carbon as global temperatures rise. The research appears in Nature Geoscience.
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LLNL scientists and collaborators quantify and model the emergent temperature sensitivity of soil organic carbon.

Simon Pang (left) and Buddhinie Jayathilake assemble and prepare a prototype bubble column electrobioreactor to test additively manufactured three-dimensional electrodes. Under their project, excess renewable electricity from wind and solar sources would be stored in chemical bonds as renewable natural gas.
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LLNL researchers and partners develop a new storage method for excess renewable electricity from wind and solar sources.

A photo taken by a scanning electron microscope shows a pit at the surface of an additively manufactured (3D-printed) stainless steel part.
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LLNL scientists publish a paper on the the mysterious world of pitting corrosion in additively manufactured (3D-printed) stainless steel 316L in seawater.

Supercomputer simulations predicting the synthesis pathways for the elusive BC8 "super-diamond", involving shock compressions of diamond precursor, inspire ongoing Discovery Science experiments at NIF.
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A team of LLNL scientists conducts multi-million atomic molecular-dynamics simulations to uncover required temperature and pressure conditions for super-diamond creation.