Topic: Scientific ML

LLNL participates in the ISC High Performance Conference (ISC24) on June 10–13.

LLNL researchers have posters and workshop papers accepted to the 13th International Conference on Learning Representations on April 24–28.

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

The February 28 event brought together over 1,400 Department of Energy scientists across multiple sites to explore how cutting-edge AI models could transform scientific research.

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

The latest episode of the Big Ideas Lab podcast investigates the use of artificial intelligence for drug discovery and other uses.

The DarkStar inverse design technique blends AI, machine learning, and advanced hydrodynamics simulations to optimize science and engineering solutions starting from the final state.

SC24, held recently in Atlanta, was a landmark event, setting new records and demonstrating LLNL's unparalleled contributions to HPC innovation and impact.

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

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

AMS is a machine learning solution embedded into scientific applications to automatically replace fine-scale simulations with ancillary models.

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.

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.

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.

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.

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.

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

LLNL researchers collaborated with Washington University in St. Louis to devise a state-of-the-art ML–based reconstruction tool for when high-quality computed tomography data is in low supply.

Merlin is an open-source workflow orchestration and coordination tool that makes it easy to build, run, and process large-scale workflows.

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.

This issue highlights some of CASC’s contributions to making controlled laboratory fusion possible at the National Ignition Facility.