Topic: Computational Science

LLNL scientists are contributing to the global fight against COVID-19 by combining AI/ML, bioinformatics, and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs.

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The White House announced the COVID-19 HPC Consortium to provide access to the world’s most powerful HPC resources that can advance the pace of scientific discovery in the fight to stop the virus.

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LLNL bested more than two dozen teams to place first overall in Challenge 1 of the DOE Grid Optimization Competition, aimed at developing a more reliable, resilient, and secure U.S. electrical grid.

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On January 31, 2020, the Sequoia supercomputer and its file system were decommissioned after nearly 8 years of remarkable service and achievements.

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Laser-fusion researchers have turned to machine-learning techniques to seek the combinations of laser pulse characteristics and target design needed to optimize target implosions for ICF.

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A multi-institutional consortium aims to speed up the drug discovery pipeline by building predictive, data-driven pharmaceutical models.

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Jorge Castro Morales likes having different responsibilities at work. He says, “I’m honored to be working with a diverse team of multidisciplinary experts to resolve very complex problems on a daily basis.”

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The paper describes the workflow driving a first-of-its-kind multiscale simulation on predictively modeling the dynamics of RAS proteins and interactions with lipids.

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Twelve projects are awarded funding for the High Performance Computing for Energy Innovation Program, which leverages DOE’s HPC facilities to improve energy efficiency and manufacturing processes.

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The HPC4EI Initiative seeks industry partners to work with DOE labs to solve key technical challenges in manufacturing and mobility.

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At the National Ignition Facility, simulations help assess the risk of damage from target debris and shrapnel dispersal during high-energy laser shots.

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LLNL researchers and colleagues are using machine learning as a virtual magnifying glass to study interesting regions of RAS protein/lipid simulations in higher detail.

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After years of preparation, LLNL’s upgraded Ares code runs a 98-billion-element simulation on 16,384 GPUs on the Sierra supercomputer.

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Computational Scientist Ramesh Pankajakshan came to LLNL in 2016 directly from the University of Tennessee at Chattanooga. But unlike most recent hires from universities, he switched from research professor to professional researcher.

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The partnership will apply DOE-fueled AI capabilities to advance transformative scientific opportunities in biomedical and public health research.

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As part of the Department of Energy’s role in the fight against cancer, scientists are building tools that use supercomputers to solve problems in entirely new ways.

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Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.

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LLNL researchers have posted another standout year securing major grants through the DOE's Technology Commercialization Fund, including one for the Radiation Field Training Simulator.

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Ryan Chen, LLNL data analyst and visualization technologist, has developed a model called the RDD Studio that provides a detailed simulation of an optimal response to a radiological dispersal device.

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In the recently launched MTV Consortium, the Lab and academia join forces to address crucial global security challenges.

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LLNL’s brain-on-a-chip may offer an effective way to evaluate the organ’s response to threats. Data analytics and HPC modeling help scientists better understand neuronal networks.

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LLNL is home to several supercomputers, including Sierra, the world's second fastest. PCMag stopped by to find out how these computers handle virtual nuclear weapons tests and weather modeling.

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Researchers from Lawrence Livermore and Berkeley Labs are using supercomputers to quantify earthquake hazard and risk across the Bay Area.

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The HPC4EI program announced 9 public/private projects awarded more than $2 million from the DOE. This program is the umbrella entity for the HPC4Mfg and HPC4Mtls programs, headed out of LLNL.

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Simulation workflows for Arbitrary Lagrangian–Eulerian (ALE) methods are highly complex and often require a manual tuning process. There is an urgent need to semi-automate this process to reduce user burden and improve productivity. To address this need, we are developing novel predictive analytics for simulations and an in situ infrastructure for integration of analytics. Our ongoing goals are to predict simulation failures ahead of time and proactively avoid them as much as possible.

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