Topic: Materials Science

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 virtual event brought together more than 500 attendees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia.

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.

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

Livermore teams are applying innovative data analysis and interpretation techniques to advance fundamental science research.

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.

An LLNL team used LANL's Trinity supercomputer for a machine-learned surrogate representation of their laser-driven fusion implosion model.

The HPC for Manufacturing Program (HPC4Mfg) announced the recipients of $1.2 million in federal funding for projects aimed at solving key manufacturing challenges through supercomputing.

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

Livermore researchers have developed an algorithm for the numerical solution of a phase-field model of microstructure evolution in polycrystalline materials. The system of equations includes a local order parameter, a quaternion representation of local orientation, and species composition. The approach is based on a finite volume discretization and an implicit time-stepping algorithm. Recent developments have been focused on modeling solidification in binary alloys, coupled with CALPHAD methodology.

LLNL researchers are developing a truly scalable first-principles molecular dynamics algorithm with O(N) complexity and controllable accuracy, capable of simulating systems of sizes that were previously impossible with this degree of 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.