Topic: Computational Science

The Generative Unconstrained Intelligent Drug Engineering (GUIDE) program accelerates development of medical countermeasure candidates to redefine biological defense.

Held for the first time in a hybrid format, the multi-day MFEM workshop drew participants from around the globe.

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.

An iconic LLNL computer code that has saved the automobile industry billions of dollars is the focus for the newest episode of the Big Ideas Lab Podcast.

LLNL is participating in the 36th annual Supercomputing Conference (SC24) in Atlanta on November 17–22, 2024.

A virtual reality training platform allows liver surgeons to prepare and train outside of the operating room.

A groundbreaking multidisciplinary team is combining the power of exascale computing with AI, advanced workflows, and GPU acceleration to advance scientific innovation and revolutionize digital design.

Learn about the game-changing potential of El Capitan and discover how it will not only transform HPC and AI but also revolutionize scientific research across multiple domains.

Follow along at your own pace through tutorials of several open-source HPC software projects.

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.

The collaboration has enabled expanding systems of the same architecture as LLNL’s upcoming exascale supercomputer, El Capitan, featuring AMD’s cutting-edge MI300A processors.

Ensuring researchers and policymakers can predict and prepare for the long-term effects of a changing climate is a central, motivating question for the Energy Exascale Earth System Model (E3SM) project.

The proposed Frontiers in Artificial Intelligence for Science, Security and Technology (FASST) initiative will advance national security; attract and build a talented workforce; harness AI for scientific discovery; address energy challenges; develop technical expertise necessary for AI governance.

This issue highlights some of CASC’s contributions to the DOE's Exascale Computing Project.

LLNL is applying ML to real-world applications on multiple scales. Researchers explain why water filtration, wildfires, and carbon capture are becoming more solvable thanks to groundbreaking data science methodologies on some of the world’s fastest computers.

Developed by LLNL and Portland State University researchers, innovative matrix-free solvers offer performance gains for complex multiphysics simulations.

In a milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer.

LLNL’s HPC capabilities play a significant role in international science research and innovation, and Lab researchers have won 10 R&D 100 Awards in the Software–Services category in the past decade.

Randles, a former Lawrence fellow and current LLNL collaborator, was recognized for “groundbreaking contributions to computational health through innovative algorithms, tools and high performance computing methods for diagnosing and treating a variety of human diseases.”

In a groundbreaking development for addressing future viral pandemics, a multi-institutional team involving LLNL researchers has successfully combined an AI-backed platform with supercomputing to redesign and restore the effectiveness of antibodies whose ability to fight viruses has been compromi

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, HPC, and AI techniques.

By taking weather variables such as wildfire, flooding, wind, and sunlight that directly impact the electrical grid into consideration, researchers can improve electrical grid model projections for a more stable future.

MuyGPs helps complete and forecast the brightness data of objects viewed by Earth-based telescopes.

The Enabling Technologies for High-Order Simulations (ETHOS) project performs research of fundamental mathematical technologies for next-generation high-order simulations algorithms.