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Wei Li and Xavier Mayali used the NanoSims to measure nitrogen incorporation and exchange at single cell resolution of harmful algal blooms from Lake Erie.
// S&T Highlights

Unexpected source of nutrients fuels growth of toxic algae from Lake Erie

LLNL scientists and collaborators investigate several Microcystis cultures collected from algal blooms in Lake Erie.

Left to right: LLNL researchers Jimmy Shen, Lars Voss and Joel Varley have software that can efficiently and effectively automate and analyze point defects in materials.
// S&T Highlights

LLNL researchers develop framework for databasing properties of crystal defects

LLNL researchers create software that can efficiently and effectively automate and analyze point defects in crystalline materials. 

An artistic impression of WASP-107b.
// S&T Highlights

Mystery of puffy exoplanet solved

A LLNL scientist and international collaborators have characterized the atmosphere of the exoplanet, WASP-107b, using a transmission spectroscopy technique.

Elizabeth Grace (LLNL, right) and Filip Grepl (ELI Beamlines, left) assemble the PROBIES diagnostic, one of many instruments that fed data to the machine-learning optimizer algorithm.
// S&T Highlights

Machine learning optimizes high-power laser experiments

LLNL international scientists and collaborators collaborate on an experiment to optimize a high-intensity, high-repetition-rate laser using machine learning.  

Amanda Randles, recipient of the 2023 ACM Prize in Computing.
// Recognition

Former Lawrence fellow Amanda Randles awarded 2023 ACM Prize in Computing

Amanda Randles was recognized for groundbreaking contributions to computational health with the 2023 ACM Prize in Computing. 

Graphic of Earth.
// S&T Highlights

Lab study on climate sensitivity earns top notch in list of influential researc…

The American Geophysical Union showcases a paper by LLNL scientists and collaborators on global climate models.

Unveiled at the International Supercomputing Conference in Germany, the June 2024 Top500 lists three systems with identical components — one computing rack each from El Capitan’s “Early Delivery System” (EDS), LLNL’s newest unclassified supercomputer RZAdams and its unclassified “sister” system Tuolumne. All three registered 19.65 petaFLOPs on the High Performance Linpack (HPL) benchmark, ranking them among the world’s 50 fastest.
// S&T Highlights

LLNL’s El Capitan debuted on new Top500 list of world’s most powerful supercomp…

Three new systems currently or soon-to-be sited at LLNL debuted on the latest Top500 list of most powerful supercomputers in the world.

Project DarkStar leverages artificial intelligence and machine learning to optimize shaped charges—explosive devices used to manipulate metals.
// S&T Highlights

Manufacturing optimized designs for high explosives

LLNL researchers couple computing capabilities and manufacturing methods to rapidly develop and experimentally validate modifications to a shaped charge.

Artwork illustrating a new study combining atomistic simulations, machine learning potential, and data-driven methods to study the chemical speciation of amorphous carbon nitride using X-ray absorption near-edge structure (XANES) spectra.
// S&T Highlights

Accelerating material characterization: Machine learning meets X-ray absorption…

LLNL scientists develop a new approach that can rapidly predict the structure and chemical composition of heterogeneous materials.

Kyle Bushick is the recipient of the 2024 Frederick A. Howes Scholar award for his work in computational science.
// Recognition

Lab scientist wins DOE Howes Scholar award for work in computational science

LLNL postdoc Kyle Bushick garners the 2024 Frederick A. Howes Scholar award for technical excellence, leadership and character in the field of computational science.