Topic: Materials Science

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.

LLNL researchers have developed a novel machine learning (ML) model that can predict 10 distinct polymer properties more accurately than was possible with previous ML models.

The second article in a series about the Lab's stockpile stewardship mission highlights computational models, parallel architectures, and data science techniques.

The first article in a series about the Lab's stockpile stewardship mission highlights the roles of computer simulations and exascale computing.

Researchers from LLNL's Energetic Materials Center and Purdue University have leveraged LLNL supercomputing to better understand the chemical reactions that detonate explosives that are “critical to managing the nation’s nuclear stockpile.”

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.

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.