Topic: Computational Science

Livermore researchers are engaged in efforts to apply correctness and formal methods to improve the reliability, reproducibility, and accuracy of the Laboratory’s high-performance computing codes.

Widely viewed as the highest recognition in HPC, the Gordon Bell Prize recognizes innovations that push the limits of computational performance, scalability and scientific impact on pressing real-world problems.

Researchers used the exascale supercomputer El Capitan to perform the largest fluid dynamics simulation ever—surpassing one quadrillion degrees of freedom in a single computational fluid dynamics problem.

Scientists at LLNL and collaborators at AMD and Columbia University have achieved a milestone in biological computing: completing the largest and fastest protein structure prediction workflow ever run, using the full power of El Capitan.

Five years strong, the MFEM workshop fosters connection and collaboration among the computational math community.

LLNL is participating in the 37th annual Supercomputing Conference (SC25) in St. Louis on November 16–21, 2025.

The latest issue of LLNL's magazine marks the 20th anniversary of the Computing Grand Challenge.

Building on our leadership in HPC and AI and our long open-source tradition, ElMerFold is a high performance framework for large-scale inference and distillation on LLNL supercomputers with OpenFold-specific optimizations.

From capturing the chaotic spray of molten metal to the turbulence of fluid flows, the exascale machine is revealing worlds that were previously beyond reach, and it’s doing so thanks to the close collaboration of hardware, software and science teams that makes LLNL uniquely equipped to lead in this space.

LLNL researchers employed an AI-driven model to predict fusion ignition days ahead of the historic 2022 shot, according to a new study in Science.

LLNL is home to the world’s most complete set of ICF modeling and simulation tools, encapsulating the intricacies of laser light interaction, electron and x-ray transport, nonequilibrium atomic physics, magnetohydrodynamics, and fusion burn.

Scientists at LLNL have helped develop an advanced, real-time tsunami forecasting system—powered by El Capitan, the world’s fastest supercomputer—that could dramatically improve early warning capabilities for coastal communities near earthquake zones.

Part of an AI framework called the Multi-Agent Design Assistant (MADA), LLNL scientists and collaborators are merging LLMs with simulation tools to interpret natural language prompts and using the platform to generate full physics simulation decks for LLNL’s MARBL multiphysics code.

As Computing’s ninth Fernbach Fellow, postdoctoral researcher Daniel Nichols will explore how AI can accelerate HPC and computational science under the mentorship of Harshitha Menon.

A new CASC paper proposes unity and clarity around foundation models in computational science, offering an implementation framework inspired by finite element methods.

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.

A new cancer drug candidate developed by LLNL, BridgeBio Oncology Therapeutics, and the Frederick National Laboratory for Cancer Research has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

LLNL and Amazon Web Services are partnering to leverage the power of AI to enhance operations at the National Ignition Facility.

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.

Researchers from LLNL, in collaboration with other leading institutions, have successfully used an AI-driven platform to preemptively optimize an antibody to neutralize SARS-CoV-2 variants.

A study led by LLNL scientists is providing new insights into the complex interactions between proteins and cell membranes, combining detailed molecular simulations and large-scale models.

In a recent study published in the Astrophysical Journal, LLNL researchers developed an innovative approach to map cosmic shear using linear algebra, statistics, and HPC.

Highlights include ML techniques for computed tomography, a scalable Gaussian process framework, safe and trustworthy AI, and autonomous multiscale simulations.

The latest issue of LLNL's magazine explains how the world’s most powerful supercomputer helps scientists safeguard the U.S. nuclear stockpile.

LLNL, Arizona State University and Michigan State University will dive deep into uncovering the compositions of 70 exoplanets through the Computing Grand Challenge Program, which allocates significant quantities of institutional computational resources to scientists to perform cutting-edge research.