(AGENPARL) – ven 14 gennaio 2022 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, Jan. 14, 2022
LLNL has received a Glassdoor Employees’ Choice Award, recognizing the Best Places to Work in 2022.
[It’s a great place to work](https://www.llnl.gov/news/lawrence-livermore-makes-glassdoors-2022-best-places-work-list)
For the fourth consecutive year, Lawrence Livermore National Laboratory has been honored with a Glassdoor Employees’ Choice Award, recognizing the Best Places to Work in 2022.
The Employees’ Choice Award, now in its 14th year, is based solely on the input of employees, who elect to provide anonymous feedback by completing a company review about their job, work environment and employer on Glassdoor, the worldwide leader on insights about jobs and companies.
LLNL is comprised of a world-class workforce of more than 8,000 of the best and brightest scientists, engineers, business professionals, innovators, skilled tradespeople, technical staff and more, many of whom are drawn to the Laboratory for what it stands for: “Science and Technology on a Mission.”
[Read More](https://www.llnl.gov/news/lawrence-livermore-makes-glassdoors-2022-best-places-work-list)
A U.S. Forest Service prescribed burn in California’s Sierra National Forest. Photo courtesy of U.S .Forest Service.
[Where there’s fire](https://scitechdaily.com/fire-may-actually-increase-long-term-carbon-storage-important-nature-based-climate-solution/)
Fire may actually increase long-term carbon storage and may serve as a nature-based climate solution.
Wildfires and prescribed burns, which can promote soil organic matter stability, may be an important nature-based climate solution to increase long-term carbon storage, according to an international team of researchers, including a scientist from Lawrence Livermore, who looked at the effect of wildfires and prescribed burns on the global carbon cycle.
Soils are the largest pool of organic carbon (C) on land, and they offer both an opportunity and a risk to climate-C feedbacks in the Earth system because of their role in the global C cycle as well as their vulnerability to disturbance.
[Read More](https://scitechdaily.com/fire-may-actually-increase-long-term-carbon-storage-important-nature-based-climate-solution/)
NIF beamlines entering the lower hemisphere of the NIF target chamber, as seen from the ground floor of the target bay.
To build a fusion reactor is essentially to create an artificial star. Scientists have been studying the physics of fusion for a century and working to harness the process for decades.
It’s a technology that could safely provide an immense and steady torrent of electricity, harnessing abundant fuel made from seawater to ignite the same reaction that powers the sun. It would produce no greenhouse gases and minimal waste compared to conventional energy sources.
One category of fusion involves confining the fusion fuel and compressing it in a tiny space with the aid of lasers. This is the approach used by Lawrence Livermore’s National Ignition Facility (NIF).
Researchers at NIF reported last summer that they achieved their best results yet — 1.3 megajoules of output from 1.9 megajoules of input — putting them closer than ever to energy-positive fusion. “We’re on the threshold of ignition,” said Tammy Ma, a LLNL plasma physicist at NIF.
In the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), the macroscale simulation runs a large system, with hundreds of proteins, at low resolution and machine learning decides which regions of the macro-model require investigation in a microscale simulation at much higher resolution. Graphic by Tim Carpenter/LLNL.
[Modeling protein behavior](https://insidehpc.com/2022/01/llnl-scientists-use-sierra-supercomputer-to-develop-cancer-model/)
Lawrence Livermore researchers and a multi-institutional team of scientists have developed a highly detailed, machine learning-backed multiscale model revealing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers.
The team simulated a one-micron-by-one-micron patch on LLNL’s Sierra supercomputer and observed how hundreds of different RAS proteins interacted with eight kinds of lipids. They created more than 100,000 smaller molecular dynamic simulations from machine learning-selected “interesting” snapshots of the larger macro model simulation, enabling them to determine the probabilities of RAS binding to other proteins with a given orientation on a cell membrane.


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