A new multiscale model incorporates both microstructural and atomistic simulations to understand barriers to ion transport in solid-state battery materials.
The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.
An LLNL-led collaboration targeted using machine learning to reduce defects and carbon emissions in steelmaking receives funding through the HPC4Mfg Program.
In a project with U.S. Steel, LLNL computational physicists built models of the hot-rolling process to run on LLNL’s HPC platforms.
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.
The Center for Non-Perturbative Studies of Functional Materials under Non-Equilibrium Conditions advances high performance computing software to support novel materials discovery.
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.
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.
StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.
The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.
LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using artificial intelligence.
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.
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.
Highlights include debris and shrapnel modeling at NIF, scalable algorithms for complex engineering systems, magnetic fusion simulation, and data placement optimization on GPUs.
Based on a discretization and time-stepping algorithm, these equations include a local order parameter, a quaternion representation of local orientation, and species composition.
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.
LLNL’s version of Qbox, a first-principles molecular dynamics code, will let researchers accurately calculate bigger systems on supercomputers.
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.