LAB REPORT

LLNL, which falls under the Energy Department, serves a wide variety of national security missions, with a primary focus on nuclear deterrence. And the national lab is applying a varied arsenal of emerging technologies to accomplish the mission, Brad Wallin, LLNL’s deputy director for strategic deterrence, reported in a recent interview with SIGNAL Media. He cited the El Capitan supercomputer and the National Ignition Facility (NIF) as two examples.

“I manage our nuclear weapons program that involves sustaining the warheads that we have in the active stockpile, as well as the modernization work we’re doing to modernize the deterrent. And we do that using various scientific tools that we maintain and advance here. In particular, for Livermore, that’s high-performance computing. We host El Capitan, which is still the fastest computer in the world according to the Top500, and NIF, which is the most energetic laser in the world, that we use to help recreate the conditions that nuclear weapons experience.”

The University of California has the most newly elected fellows to the American Association for the Advancement of Science (AAAS), the world’s largest general scientific society.

Lawrence Livermore National Laboratory director Kim Budil, a UC Davis alumna, leads the development and implementation of the laboratory’s scientific vision, goals and objectives and serves as the laboratory’s highest-level liaison with the Department of Energy, National Nuclear Security Administration, the LLNS Board of Governors, the University of California and other government, public and private organizations.

Robert S. Maxwell, a UC Davis and UC Santa Barbara alumnus, was elected a fellow in recognition of nearly three decades of leadership and significant contributions in materials chemistry related to national security. Maxwell currently leads numerous high-consequence efforts at the lab in partnership with National Nuclear Security Administration (NNSA) production agencies, including the Polymer Enclave, which aims to accelerate product realization for specific components while demonstrating advanced manufacturing techniques that can be applied across the NNSA enterprise, enabling more rapid delivery and significant cost savings.

Sometimes, scientists don’t see asteroids coming until they enter our planet's atmosphere, said Katie Kumamoto, leader of planetary defense work at Lawrence Livermore National Laboratory, pointing to a 2013 incident in Russia. An asteroid the size of a house exploded over the city of Chelyabinsk with the force of 440,000 tons of TNT, damaging buildings and injuring more than 1,600 people, according to NASA.

Kumamoto said that although potentially hazardous asteroids are bigger than a football field, they can be difficult to spot because they are relatively small and dark objects moving quickly in the vast expanse of space. Some asteroids can also be hidden by the glare of sunlight, making it easy for Earth-bound telescopes to miss them, according to NASA.

“It’s certainly the largest threat in planetary defense, right?” said Kumamoto. “Everything about all of our mitigation strategies, they all depend on us having a long warning time.”

Researchers and government agencies are turning to artificial intelligence to solve one of spaceflight’s fastest-growing problems: tracking debris in the vast stretch between Earth and the Moon. With more than 100 lunar missions planned for the coming decade, the region known as cislunar space is about to get far more crowded, and current tracking systems were never designed to monitor objects at those distances. A wave of new AI-driven tools, open datasets, and sensor experiments now aims to close that gap before collisions put future missions at risk.

AI models are only as good as the data they train on, and cislunar tracking has historically suffered from a severe data shortage. A recently published benchmark dataset on arXiv addresses that directly. The dataset, produced by contributors affiliated with Lawrence Livermore National Laboratory’s SSAPy ecosystem, contains one million numerically propagated cislunar trajectories. Each trajectory is propagated for up to six years, giving machine learning researchers a rich foundation for testing orbit prediction algorithms, anomaly detection, and classification models.

The moment nuclear material is produced, processed or purified, it sets off a hidden countdown, marked by the half-life of its radioactive atoms as they begin to decay. For scientists tracking the origins of these substances, decoding this natural clock is crucial for verifying material histories in support of global security efforts.

In a new study published in the Journal of Radioanalytical and Nuclear Chemistry, researchers at Lawrence Livermore National Laboratory (LLNL) and collaborators at the University of New Mexico and the University of Michigan offer a novel approach for measuring the age of nuclear materials. Relying on ultra-cold microcalorimeters operating at 0.01 Kelvin, the team successfully determined the age of a 100-day old plutonium sample that weighed only 26 trillionths of a gram by measuring the decay-rate ratio of plutonium-241 (²⁴¹Pu) to its decay product americium-241 (²⁴¹Am).

Accurate nuclear age-dating helps determine when nuclear material was made or last processed, which is important for investigating the origin of samples for nuclear forensics and safeguards.