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Cover of book with title-case lettering
// S&T Highlights
This book includes chapters on machine learning, image analysis, and data segmentation contributed by Livermore scientists.
Cover of book with title-case lettering
// Journal Covers
This book includes chapters on machine learning, image analysis, and data segmentation contributed by Livermore scientists.
Cover of Materials Today journal with illustration of spheres and streak ending at hot spot
// Journal Covers
Demonstrates a multiscale simulation framework coupling that can quantitatively predict tailored microstructure formation in laser processed Ti-Nb.
Two rows of four images of graphs with complex curves
// Journal Covers
This study computationally explores using high-resolution inelastic x-ray scattering at x-ray free electron laser facilities to probe the temperature of a material in high-pressure shock or ramp compression.
Award title, above, with seven people facing camera below
// Recognition
For the fourth consecutive year, Lawrence Livermore National Laboratory has been honored with a Glassdoor Employees’ Choice Award, recognizing the Best Places to Work in 2022.
Drawing of Pandora (right), planet (center), and host star (left)
// S&T Highlights
NASA has approved the Pandora mission, co-led by the Goddard Space Flight Center and Livermore.
Three simulations of hundreds of proteins in sheets superimposed on image of supercomputer
// S&T Highlights
A multi-institutional team has developed a multiscale model revealing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to many cancers.
Richard Klein next to AAS logo
// Recognition
Livermore physicist Richard Klein has been selected as a 2022 fellow of the American Astronomical Society.
Firefighter walking through burned landscape, with tree on left
// S&T Highlights
Wildfires and prescribed burns, which can promote soil organic matter stability, may be an important nature-based climate solution to increase long-term carbon storage.
Diagram of virtual representations of cancer patients
// S&T Highlights
A multi-institutional team has proposed a framework for digital twin models of cancer patients.