(AGENPARL) – Fri 23 May 2025 A weekly compendium of media reports on science and technology achievements
at Lawrence Livermore National Laboratory. Though the Laboratory reviews
items for overall accuracy, the reporting organizations are responsible for
the content in the links below.
LLNL Report,
NIF beamlines entering the lower hemisphere of the target chamber, as seen
from the ground floor of the target bay. (Photo: Damien Jemison)
NIF ramps up

Laser-powered fusion experiment more than doubles its power output

The world’s only net-positive fusion experiment has been steadily ramping
up the amount of power it produces, TechCrunch has learned.
In recent attempts, the team at the U.S. Department of Energy’s National
Ignition Facility (NIF) increased the yield of the experiment, first to 5.2
megajoules and then to 8.6 megajoules, according to a source with knowledge
of the experiment.
The new results are significant improvements over the historic experiment in
2022, which was the first controlled fusion reaction to generate more energy
than it consumed.
The 2022 shot generated 3.15 megajoules, a small bump over the 2.05
megajoules that the lasers delivered to the BB-sized fuel pellet.
None of the shots to date have been effective enough to feed electrons back
into the grid, let alone to offset the energy required to power the entire
facility — the facility wasn’t designed to do that. The first
net-positive shot, for example, required 300 megajoules to power the laser
system alone. But they are continued proof that controlled nuclear fusion is
more than hypothetical.
Read More

Laser-powered fusion experiment more than doubles its power output

 ALS CURE Project co-founder Mike Piscotty presents a check to Livermore Lab
Foundation representatives Dona Crawford and Sally Allen in 2020. A new
collaborative effort continues this work in 2025. 
LLNL takes on ALS
The ALS Network, in partnership with the ALS CURE Project, Livermore Lab
Foundation, RDM Positive Impact Foundation, and Stanford University, is
supporting a $300,000 collaborative research effort at Lawrence Livermore
National Laboratory (LLNL).
Led by Priyadip Ray, Ph.D., LLNL; Richard Reimer, M.D., Stanford University
and Veterans Administration; Jennifer Wilson, PhD, University of California
Los Angeles; and Kevin Grimes, M.D., Stanford University, the project seeks
to better understand environmental factors, specifically medications, and
their effects on ALS and Parkinson’s diseases.
“Medications typically have well defined therapeutic targets but often also
have off-target effects,” said Dr. Ray. “Our long-term goal is to
identify associations between medications and progression of
neurodegenerative diseases through the analysis of electronic health records
(EHRs). As an initial step, we have focused on ALS as a proof of principle
study.”
The funds will allow researchers to expand previous work funded by the
Livermore Lab Foundation with support from the ALS CURE Project, which
focused on EHR data for more than 20,000 veterans diagnosed with ALS — a
group that for unknown reasons develops ALS up to 1.5 times the rate of the
general population.
Read More
Designed to optimize the convergence of AI and high-performance computing, El
Capitan’s accelerated processing units deliver unmatched computational
performance. (Photo: Garry McLeod/LLNL)
Keeping computing competitive
As Congress scrambles to put together a budget deal, some tech experts are
worried about the ability of modern hardware to keep up with the demands of
powerful AI tools — and arguing that government has a bigger role to play
in keeping American computing globally competitive.
“Other countries are moving quickly, and without a national strategy, the
U.S. risks falling behind,” wrote veteran computer scientist Jack Dongarra
of the University of Tennessee. Dongarra argues that “a U.S. national
strategy should include funding new machines and training for people to use
them,” as well as “partnerships with universities, national labs and
private companies.”
Historically, the U.S. has shown a willingness to make significant
investments in what’s broadly known as “high-performance computing,” or
supercomputers that often use millions of processors in concert to execute
operations at lightning speed.
The Exascale Computing Project, which spanned the Obama, Trump 1.0 and Biden
eras, came to a conclusion last year with nearly $2 billion spent on a
massive supercomputing effort that led to the El Capitan exascale computer at
the Lawrence Livermore National Laboratory coming online in February.
Read More
Researchers from LLNL and their collaborators successfully used an AI-driven
platform to optimize an antibody for SARS-CoV-2. (Credit: Janelle Cataldo)
Including AI (and healthy skepticism) in chemistry
https://cen.acs.org/physical-chemistry/computational-chemistry/guide-navigating-AI-chemistry-hype/103/web/2025/05
The world is awash with news about artificial intelligence tools, including
those intended to help chemists. But are the tools useful, a threat, or even
worth the attention? It’s hard to know, especially when developers are
making bold, exaggerated claims. This guide to AI in chemistry aims to help
researchers steer through the complicated maze of tools available.
General-purpose LLMs such as GPT also can answer chemistry questions that
look like natural language, but they may be less successful than specialized
models when it comes to structures and equations. Jonathan Allen, a senior
informatics researcher at Lawrence Livermore National Laboratory (LLNL),
suggests that such tools are “glorified Google searches” with
“more-efficient summarization.”
“Property prediction of molecules is challenging,” Allen says. “The
space of molecules is very large, and the amount of experimental data
that’s been collected relative to the things that can be made is very
small.” Generative models can propose seemingly plausible molecules, but
many of them are not actually synthesizable, he adds. “A healthy dose of
skepticism has to be front and center” in assessing their value, Allen
says.
Read More
https://cen.acs.org/physical-chemistry/computational-chemistry/guide-navigating-AI-chemistry-hype/103/web/2025/05
Together with Los Alamos and Lawrence Livermore National Labs, the University
of California is funding ambitious research to develop new AI approaches in
genomics, quantum materials discovery and geothermal energy.
Toward the frontier of AI
https://www.universityofcalifornia.edu/news/uc-awards-18-million-scale-ambition-and-impact-ai-science
The University of California has awarded $18 million in multicampus research
grants, in partnership with UC-managed national laboratories, to advance the
frontiers of artificial intelligence in areas of strategic importance and
technological innovation.
The Artificial Intelligence (AI) Science at Scale initiative provides three
grants of $6 million over three years to develop new AI approaches in
genomics, quantum materials discovery and geothermal energy. The winning
teams are composed of UC faculty across a wide range of disciplines,
representing nearly every UC campus. This research is funded by fee income
the university receives for managing the Los Alamos and Lawrence Livermore
National Laboratories.
“The rapid growth of artificial intelligence is accelerating both the
opportunities for, and threats to, the United States’ longstanding economic
leadership. To help keep America in the lead, the University of California is
scaling up its commitments in critical emerging areas of scientific research,
such as AI, and moving forward with unprecedented speed to fund targeted
research that fosters innovation,” said Theresa Maldonado, UC vice
president of research & innovation.
Read More
https://www.universityofcalifornia.edu/news/uc-awards-18-million-scale-ambition-and-impact-ai-science
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Administration.
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