Science and Technology Highlights

Science on Saturday is a series of science lectures for middle and high school students. Each topic highlights cutting-edge science occurring at the Lawrence Livermore National Laboratory (LLNL). The talks are presented by LLNL scientists in collaboration with local area high school science teachers. These presentations are offered in several locations. Admission is free of charge. Seating is on a first-come first-served basis. Location: Bankhead Theater (2400 First St, Livermore, CA 94550) Two Presentations: 9:30 AM & 11:15 AM; Download Flyer
molecular dynamics simulation shows the fast diffusion of hydrogen ions
// S&T Highlights
A research team successfully produced superionic ice.
Person uses virtual reality technology
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Virtual reality technology finds real-world applications at the National Ignition Facility.
Scientists examie power distribution grid simulation
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A Livermore team has taken a key step toward creating a commercial tool that utilities could use to modernize the grid.
Lake Tahoe in winter
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The Sierra snowpack will decline in years to come because of climate change effects.
Lasers beaming into a gold rugby hohlraum holding an aluminum fuel capsule
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Experiments using a hohlraum shaped like a rugby ball have significantly boosted the amount of laser-induced energy absorbed by an inertial confinement fusion fuel capsule.
A dwarf galaxy
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Lawrence Livermore nuclear scientists for the first time have accurately predicted the properties of polarized thermonuclear fusion.
The element zirconium
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New research reveals that the radioactive isotope zirconium-88 is an excellent neutron absorber.
Simulation of a fusion reactor
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LLNL's Center for Applied Scientific Computing collaborates with its fusion researchers.
Scientists talk at American Physics Society meeting social event
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The Laboratory was well represented at the recent 60th annual American Physical Society (APS) Division of Plasma Physics meeting.
Model of innate immune response
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Livermore and University of Vermont researchers are exploring how deep reinforcement learning can discover therapeutic drug strategies for sepsis.