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LLNL researchers unleash machine learning in designing advanced lattice structures
Aug. 22, 2024 -
Characterized by their intricate patterns and hierarchical designs, lattice structures hold immense potential for revolutionizing industries ranging from aerospace to biomedical engineering, due to their versatility and customizability. However, the complexity of these structures and the vast design space they encompass have posed significant hurdles for engineers and scientists, and...
LLNL, DOD, NNSA dedicate Rapid Response Laboratory and supercomputing system to accelerate biodefense
Aug. 15, 2024 -
LLNL recently welcomed officials from the Department of Defense (DOD) and National Nuclear Security Administration (NNSA) to dedicate a new supercomputing system and Rapid Response Laboratory (RRL). DOD is working with NNSA to significantly increase the computing capability available to the national biodefense programs. The collaboration has enabled expanding systems of the same architecture...
LLNL and BridgeBio announce trials for supercomputing-discovered cancer drug
June 6, 2024 -
In a substantial milestone for supercomputing-aided drug design, LLNL and BridgeBio Oncology Therapeutics (BridgeBio) today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer. The development of the new drug—BBO-8520—is the result of collaboration among LLNL, BridgeBio and the National Cancer...
The Laboratory’s habit of innovation
June 4, 2024 -
LLNL’s HPC and data science 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. The latest issue of Science & Technology Review features several award-winning projects, including ZFP and CANDLE: (1) ZFP introduces a new method of compressing large data sets...
Manufacturing optimized designs for high explosives
May 13, 2024 -
When materials are subjected to extreme environments, they face the risk of mixing together. This mixing may result in hydrodynamic instabilities, yielding undesirable side effects. Such instabilities present a grand challenge across multiple disciplines, especially in astrophysics, combustion, and shaped charges—a device used to focus the energy of a detonating explosive, thereby creating a...
Accelerating material characterization: Machine learning meets X-ray absorption spectroscopy
May 10, 2024 -
LLNL scientists have developed a new approach that can rapidly predict the structure and chemical composition of heterogeneous materials. In a new study in ACS Chemistry of Materials, Wonseok Jeong and Tuan Anh Pham developed a new approach that combines machine learning with X-ray absorption spectroscopy (XANES) to elucidate the chemical speciation of amorphous carbon nitrides. The research...
GUIDE team develops approach to redesign antibodies against viral pandemics
May 8, 2024 -
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 compromised by viral evolution. The team’s research is published in the journal Nature and showcases a...
Welcome new DSI team members
April 2, 2024 -
When Data Science Institute (DSI) director Brian Giera and deputy director Cindy Gonzales began planning activities for fiscal year 2024 and beyond, they immediately realized that LLNL’s growth in data science and artificial intelligence (AI)/machine learning (ML) research requires corresponding growth in the DSI’s efforts. “Our field is booming,” Giera states. “The Lab has a stake in the...
Will it bend? Reinforcement learning optimizes metamaterials
Dec. 13, 2023 -
Lawrence Livermore staff scientist Xiaoxing Xia collaborated with the Technical University of Denmark to integrate machine learning (ML) and 3D printing techniques. The effort naturally follows Xia’s PhD work in materials science at the California Institute of Technology, where he investigated electrochemically reconfigurable structures. In a paper published in the Journal of Materials...
Data Science Summer Institute hosts student interns from Japan
Oct. 13, 2023 -
The Data Science Summer Institute (DSSI) hosted summer student interns from Japan on-site for the first time, where the students worked with Lab mentors on real-world projects in AI-assisted bio-surveillance and automated 3D printing. From June to September, the three students—Raiki Yoshimura, Shinnosuke Sawano and Taisei Saida—lived in rental apartments near the Lab and worked at the Lab on...
LLNL, University of California partner for AI-driven additive manufacturing research
Sept. 27, 2023 -
Grace Gu, a faculty member in mechanical engineering at UC Berkeley, has been selected as the inaugural recipient of the LLNL Early Career UC Faculty Initiative. The initiative is a joint endeavor between LLNL’s Strategic Deterrence Principal Directorate and UC national laboratories at the University of California Office of the President, seeking to foster long-term academic partnerships and...
Making machine learning safer for biomedicine
Aug. 15, 2023 -
It’s hard to understate the impact machine learning will have on biomedicine. The ability to train computers to spot patterns by analyzing large, complex datasets is driving discoveries in heart disease, cancer, neurodegenerative diseases and more. For instance, Argonne National Laboratory (ANL) has used machine learning to aid cancer research and accelerate COVID-19 antiviral discovery. One...
Explainable artificial intelligence can enhance scientific workflows
July 25, 2023 -
As ML and AI tools become more widespread, a team of researchers in LLNL’s Computing and Physical and Life Sciences directorates are trying to provide a reasonable starting place for scientists who want to apply ML/AI, but don’t have the appropriate background. The team’s work grew out of a Laboratory Directed Research and Development project on feedstock materials optimization, which led to...
Machine learning reveals refreshing understanding of confined water
July 24, 2023 -
LLNL scientists combined large-scale molecular dynamics simulations with machine learning interatomic potentials derived from first-principles calculations to examine the hydrogen bonding of water confined in carbon nanotubes (CNTs). They found that the narrower the diameter of the CNT, the more the water structure is affected in a highly complex and nonlinear fashion. The research appears on...
Celebrating the DSI’s first five years
May 18, 2023 -
View the LLNL Flickr album Data Science Institute Turns Five.
Data science—a field combining technical disciplines such as computer science, statistics, mathematics, software development, domain science, and more—has become a crucial part of how LLNL carries out its mission. Since the DSI’s founding in 2018, the Lab has seen tremendous growth in its data science community and has invested...
Fueling up hydrogen production
April 3, 2023 -
Through machine learning, an LLNL scientist has a better grasp of understanding materials used to produce hydrogen fuel. The interaction of water with TiO2 (titanium oxide) surfaces is especially important in various scientific fields and applications, from photocatalysis for hydrogen production to photooxidation of organic pollutants to self-cleaning surfaces and biomedical devices. However...
Scientists develop model for more efficient simulations of protein interactions linked to cancer
March 28, 2023 -
LLNL scientists have developed a theoretical model for more efficient molecular-level simulations of cell membranes and their lipid-protein interactions, part of a multi-institutional effort to better understand the behavior of cancer-causing membrane proteins. Developed under an ongoing collaboration by the Department of Energy and the National Cancer Institute (NCI) aimed at modeling cell...
From plasma to digital twins
March 13, 2023 -
LLNL's Nondestructive Evaluation (NDE) group has an array of techniques at its disposal for inspecting objects’ interiors without disturbing them: computed tomography, optical laser interferometry, and ultrasound, for example, can be used alone or in combination to gauge whether a component’s physical and material properties fall within allowed tolerances. In one project, the team of NDE...
New HPC4EI project to create 'digital twin' models for aerospace manufacturing
Jan. 19, 2023 -
A partnership involving LLNL aimed at developing “digital twins” for producing aerospace components is one of six new projects funded under the HPC for Energy Innovation (HPC4EI) initiative, the Department of Energy’s Office of Energy Efficiency and Renewable Energy announced. Sponsored by the HPC4Manufacturing (HPC4Mfg) Program, one of the pillars of HPC4EI, the collaboration between LLNL...
ML model instantly predicts polymer properties
Nov. 30, 2022 -
Hundreds of millions of tons of polymer materials are produced globally for use in a vast and ever-growing application space with new material demands such as green chemistry polymers, consumer packaging, adhesives, automotive components, fabrics and solar cells. But discovering suitable polymer materials for use in these applications lies in accurately predicting the properties that a...