Topic: Materials Science

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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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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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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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 held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12, co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute.

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The Center for Non-Perturbative Studies of Functional Materials under Non-Equilibrium Conditions advances high performance computing software to support novel materials discovery.

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The Department of Energy announced awards of $3.7 million for 13 new High Performance Computing for Energy Innovation (HPC4EI) projects, including a collaboration involving LLNL targeted at improving CO2 conversion.

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Research conducted on the Quartz supercomputer highlights findings made by scientists that reveal a missing aspect of the physics of hotspots in TATB and other explosives.

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StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.

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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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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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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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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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Highlights include debris and shrapnel modeling at NIF, scalable algorithms for complex engineering systems, magnetic fusion simulation, and data placement optimization on GPUs.

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Based on a discretization and time-stepping algorithm, these equations include a local order parameter, a quaternion representation of local orientation, and species composition.

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This scalable first-principles MD algorithm with O(N) complexity and controllable accuracy is capable of simulating systems that were previously impossible with such accuracy.

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LLNL’s version of Qbox, a first-principles molecular dynamics code, will let researchers accurately calculate bigger systems on supercomputers.

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A new algorithm for use with first-principles molecular dynamics codes enables the number of atoms simulated to be proportional to the number of processors available.

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