Topic: Computational Science

A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for HPC-Based COVID-19 Research.

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The latest issue of LLNL's Science & Technology Review magazine highlights the work already accomplished with the Sierra supercomputer and what's to come.

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At the 2020 LLNL Computing Expo, Jim Brase outlined the Lab’s predictive biology efforts for new therapeutics, HPC capabilities, and making those resources available to researchers.

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LLNL will collaborate with Machina Labs to apply ML to aluminum sheet metal processing for aerospace and automotive applications. Five recently announced LLNL-led projects will be funded by HPC4EI.

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Ruby, a 6-petaflop cluster, will be used for the stockpile stewardship mission, open science, and the search for therapeutic drugs and antibodies against SARS-CoV-2.

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The scientific computing and networking leadership of 17 DOE national labs will be showcased at SC20, taking place Nov. 9-19 for the first time via a completely virtual format.

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Funded by the CARES Act, LLNL's new computing cluster, Mammoth, will be used to perform genomics analysis, nontraditional simulations, and graph analytics required by scientists working on COVID-19.

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The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.

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Funding by the CARES Act enabled LLNL and industry partners to more than double the speed of the Corona supercomputing cluster to in excess of 11 petaFLOPS of peak performance.

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To solve a 100-year puzzle in metallurgy about why single crystals show staged hardening while others don’t, LLNL scientists performed atomistic simulations at the limits of supercomputing.

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LLNL pairs 3D-printed human brain vasculature with computational flow simulations to understand tumor cell attachment to blood vessels, a step in secondary tumor formation during cancer metastasis.

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LLNL and Duke University combine 3D bioprinting and computational flow models to analyze tumor cell behavior and the cells’ attachment to the vascular endothelium.

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An LLNL team has published new simulations of a magnitude 7.0 earthquake on the Hayward Fault—the highest-ever resolution ground motion simulations from such an event on this scale.

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LLNL researchers and collaborators have combined machine learning, 3D printing, and HPC simulations to accurately model blood flow in the aorta.

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This video provides an overview of LLNL projects in which data scientists work with domain scientists to address major challenges in healthcare.

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In the HPC4EI project, LLNL and OxEon Energy will reduce the number of reactor tubes used to convert natural gas to liquid fuel, to lower cost and increase performance of synthetic fuel production.

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Surrogate models supported by neural networks could lead to new insights in complicated physics problems such as inertial confinement fusion.

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An LLNL-led team proposes a DL approach aimed at improving the reliability of classifier models for predicting disease types from diagnostic images.

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LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using artificial intelligence.

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To help accelerate discovery of therapeutic antibodies or antiviral drugs for SARS-CoV-2, LLNL has launched a searchable data portal to share its COVID-19 research with scientists and the public.

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LLNL's Jay Thiagarajan joins the Data Skeptic podcast to discuss his recent paper "Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models." The episode runs 35:50.

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Combining computer simulations with ultra-high-speed X-ray imaging, LLNL researchers have discovered a way to reduce defects in parts built through a laser-based metal 3D-printing process.

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In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory."

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An LLNL team developed ML tools that extract and structure information from the text and figures of nanomaterials articles using NLP, image analysis, computer vision, and visualization techniques.

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