Topic: Computational Science

The latest generation of a laser beam–delay technique owes its success to collaboration, dedication, and innovation.

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An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations.

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The Data Science Institute's career panel series continued on June 28 with a discussion of LLNL’s COVID-19 research and development. Four data scientists talked about their work in drug screening, protein–drug compounds, antibody–antigen sequence analysis, and risk factor identification.

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For the first time in the DSC series since the COVID-19 pandemic began in 2020, Lab mentors visited the college campus to provide in-person guidance for five teams of UC Merced students.

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The Accelerating Therapeutic Opportunities in Medicine (ATOM) consortium is showing “significant” progress in demonstrating that HPC and machine learning tools can speed up the drug discovery process, ATOM co-lead Jim Brase said at a recent webinar.

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Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium.

People Highlight

As group leader and application developer in the Global Security Computing Applications Division, Jarom Nelson develops intrusion detection and access control software.

People Highlight

One of the most widely used tactical simulations in the world, JCATS is installed in hundreds of U.S. military and civilian organizations, in NATO, and in more than 30 countries.

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The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.

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A new multiscale model incorporates both microstructural and atomistic simulations to understand barriers to ion transport in solid-state battery materials.

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From molecular screening, a software platform, and an online data to the computing systems that power these projects.

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LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.

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The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.

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This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.

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Upgraded with the C++ programming language, VBL provides high-fidelity models and high-resolution calculations of laser performance predictions.

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LLNL researchers and collaborators have developed a highly detailed, ML–backed multiscale model revealing the importance of lipids to RAS, a family of proteins whose mutations are linked to many cancers.

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An LLNL-led collaboration targeted using machine learning to reduce defects and carbon emissions in steelmaking receives funding through the HPC4Mfg Program.

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libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

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The MFEM software library provides high-order mathematical algorithms for large-scale scientific simulations. An October workshop brought together MFEM’s global user and developer community for the first time.

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An LLNL mathematician and collaborators have developed a machine learning–based technique capable of deriving a mathematical model for the motion of binary black holes from gravitational wave data.

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In a project with U.S. Steel, LLNL computational physicists built models of the hot-rolling process to run on LLNL’s HPC platforms.

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LLNL will lend its expertise in vaccine research and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.

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A new version of the Energy Exascale Earth System Model (E3SM) is 2x faster than its earlier version released in 2018.

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CASC and the Data Science Institute welcomed a new academic partner to the 2021 Data Science Challenge program: the University of California Riverside campus.

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