Topic: Materials Science

Researchers at Brown University, LLNL, and Simula Research Laboratory have developed a new algorithm to help optimizers arrive at solutions in fewer iterations, saving valuable computing time.

Using the Sierra supercomputer, an LLNL team has made significant progress in understanding how microscopic hot spots form in insensitive high explosives based on TATB.

The team used a Bayesian approach to quantify metal strength uncertainty with tantalum and two common explosive materials and integrated it into a coupled metal/high-explosive model.

In a groundbreaking development for computational science, a team of Tri-Lab researchers has unveiled a revolutionary approach to molecular dynamics simulations using the Cerebras Wafer-Scale Engine, the world’s largest computer chip.

The event attracted more than 60 attendees from diverse sectors and featured discussions aimed at fostering new collaborations with various DOE offices and national labs.

Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.

A new component-wise reduced order modeling method enables high-fidelity lattice design optimization.

A new collaboration will leverage advanced LLNL-developed software to create a “digital twin” of the near-net shape mill-products system for producing aerospace parts.

The MAPP incorporates multiple software packages into one integrated code so that multiphysics simulation codes can perform at scale on present and future supercomputers.

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.

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.