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The Star That Wouldn’t Die

Berkeley Lab scientists were part of a team that helped to decipher one of the most bizarre spectacles ever seen in the night sky: A supernova that refused to stop shining, remaining bright far longer than an ordinary stellar explosion. What caused the event is puzzling.

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Toward High-Power, High-Efficiency LEDs

Using predictive atomistic calculations and supercomputers at NERSC, researchers found that incorporating boron into InGaN material can make the material more efficient at producing light.

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Colliding Neutron Stars

Scientists have obtained the first measurement of the merger of two neutron stars and its explosive aftermath. Computer simulations at NERSC are critical for understanding the event, which could provide valuable insights into the origin of universe’s heavy elements.

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Science DMZ for Medical Research Data

As medicine becomes more data-intensive, a Medical Science DMZ design proposed by Berkeley Lab's Sean Peisert and Eli Dart could provide a secure solution for medical science data transfers.

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Reconstructing Nanoscale Virus Features

CAMERA team contributed key algorithms which helped scientists achieve a goal proposed more than 40 years ago– using angular correlations of X-ray snapshots from non-crystalline molecules to determine 3D structure of biological objects.

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Regional Earthquake Risk & Hazards

Researchers are building the first-ever end-to-end simulation code to precisely capture the geology and physics of regional earthquakes, and how the shaking impacts buildings.

News

  • BInGaN

    NERSC Resources Help Predict New Material for High-Power, High-Efficiency LEDs

    November 22, 2017: Using predictive atomistic calculations and high-performance supercomputers at NERSC, University of Michigan researchers found that incorporating the element boron into the widely used InGaN (indium-gallium nitride) material can keep electrons from becoming too crowded in LEDs, making the material more efficient at producing light. Read More »

  • Aydin

    GraphBLAS: Building Blocks For High Performance Graph Analytics

    November 21, 2017: After nearly five years of collaboration between researchers in academia, industry and national research laboratories—including Berkeley Lab's Aydın Buluç—GraphBLAS, a collection of standardized building blocks for graph algorithms in the language of linear algebra, is publicly available. Read More »

  • Prabhat

    Deep Learning for Science: A Q&A with NERSC's Prabhat

    November 10, 2017: In this Q&A with Prabhat, who leads the Data and Analytics Services Group at NERSC and has been instrumental in several projects exploring opportunities for deep learning in science, he talks about the history of deep learning and machine learning and the unique challenges of applying these data analytics tools to science. Read More »