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Berkeley Lab Scientists Create Machine Learning Pipeline for Interpreting Large Tomography Datasets

3D rendering of six regions obtained via the machine learning pipeline developed by Berkeley Lab scientists to improve cryo-ET image data analysis. Each region is labeled by a unique color.

Berkeley Lab’s Ushizima Honored with PMWC Pioneer Award

The colors of Alzheimer’s abnormal proteins: AT8, AT100 and MC1 quantitative heatmap registered to blockface (first column), MRI (second column) of patient “Case 2.” The third column depicts the respective 3D renderings of each tau map. AT8 detected higher amounts of tau than AT100 and MC1. (Credit: Daniela Ushizima, Berkeley Lab)

Perlmutter Results Show Progress in Quantum Information Science

A supercomputer with a mural that shows wisps of orange clouds that

New Search Method Expands Horizon in Hunt for New Polymer Electrolytes

Most electric cars are powered by lithium-ion batteries, which rely on polymer electrolytes to conduct lithium ions and produce energy. Improved polymer electrolytes could improve battery performance in electric cars.

The SciData Division’s Jean Luca Bez Receives BSSw Honorable Mention

Bert de Jong Named 2023 Oppenheimer Science and Energy Leadership Program Fellow

Bert de Jong

EQSIM Shakes Up Earthquake Research at the Exascale Level

Figure 1. The multiscale computational challenge of fault-to-structure simulations starting from the earthquake source, continuing through regional-scale wave propagation in a heterogeneous earth at a scale of hundreds of kilometers (“Regional geophysics domain”) and ending at local interaction between complex incident seismic waves with a soil-structure system at a scale of 30–50 m (“Local engineering system domain”). (Credit: David McCallen)

SC22 Recap: For Berkeley Lab, a Gordon Bell Prize Win and More

CS Staff to Present at 2022 American Geophysical Union Meeting

Berkeley Lab’s Networking Middleware GASNet Turns 20

GASNet

HUNTRESS: A Step Forward for Computational Oncology Data Analysis

A tumor progression tree, inferred by HUNTRESS, represents nine clonal populations of high-grade serous ovarian cancer. (Credit: Can Kizilkale, Berkeley Lab)

SCGSR Fellow Brings Astrophysics Data Skills to Berkeley Lab and Black Holes

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