Data Science in the News

LLNL researchers unleash machine learning in designing advanced lattice structures

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...

ISCP projects make machine learning advantages tangible

Data science tools are not only rapidly taking hold across disciplines, they are constantly evolving. The applications, services, and techniques one cohort of scientists and engineers may have learned could be out of date by the next cohort, especially as machine learning (ML) and artificial intelligence (AI) tools become commonplace. To keep employees abreast of the latest tools, two data...

AI, fusion, and national security with Brian Spears (VIDEO)

This episode of the Eye on AI podcast delves into the cutting-edge world of AI and high-performance computing with Brian Spears, director of LLNL's AI Innovation Incubator. The episode is presented here as a video with the following description: "Brian shares his experience in driving AI into national security science and managing the nation’s nuclear stockpile. With a PhD in mechanical...

The surprising places you’ll find machine learning (VIDEO)

LLNL data scientists are applying ML to real-world applications on multiple scales. A new DSI-funded video highlights research at the nanoscale (developing better water treatment methods by predicting the behavior of water molecules under the extremely confined conditions of nanotubes); mesoscale (determining the likelihood and location of a dangerous wildfire-causing phenomenon called arcing...

LLNL and BridgeBio announce trials for supercomputing-discovered cancer drug

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...

Statistical framework synchronizes medical study data

The risks and benefits of heart surgery, chemotherapy, vaccination, and other medical treatments can change based on the time of day they are administered. These variations arise in part due to changes in gene expression levels throughout the 24-hour day-night cycle, with around 50% of genes displaying oscillatory behavior. To evaluate new therapies, investigators study how a gene’s...

Manufacturing optimized designs for high explosives

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...

Harnessing the power of AI for a safe and secure future (VIDEO)

LLNL, alongside the Department of Energy’s (DOE’s) 17 national labs, is harnessing the transformative potential of AI for a safer, more secure future. In 2022, LLNL made history by achieving fusion ignition, marking a pivotal moment for national security and clean energy. While AI continues to unlock new insights into fusion, through the combination of cutting-edge computer modeling...

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

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...

Igniting scientific discovery with AI and supercomputing (VIDEO)

LLNL’s fusion ignition breakthrough, more than 60 years in the making, was enabled by a combination of traditional fusion target design methods, high-performance computing (HPC), and AI techniques. The success of ignition marks a significant milestone in fusion energy research, and was facilitated in part by the precision simulations and rapid experimental data analysis only possible through...

Welcome new DSI team members

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...

Predicting climate change impacts on infrastructure (VIDEO)

At LLNL, electrical grid experts and climate scientists work together to bridge the gap between infrastructure and climate modeling. By taking weather variables such as wildfire, flooding, wind, and sunlight that directly impact the electrical grid into consideration, researchers can improve electrical grid model projections for a more stable future. In a new video, LLNL computer scientist...

Machine learning tool fills in the blanks for satellite light curves

When viewed from Earth, objects in space are seen at a specific brightness, called apparent magnitude. Over time, ground-based telescopes can track a specific object’s change in brightness. This time-dependent magnitude variation is known as an object’s light curve, and can allow astronomers to infer the object’s size, shape, material, location, and more. Monitoring the light curve of...

Will it bend? Reinforcement learning optimizes metamaterials

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

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

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...

Explainable artificial intelligence can enhance scientific workflows

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

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...

High-performance computing, AI and cognitive simulation helped LLNL conquer fusion ignition

For hundreds of LLNL scientists on the design, experimental, and modeling and simulation teams behind inertial confinement fusion (ICF) experiments at the National Ignition Facility, the results of the now-famous Dec. 5, 2022, ignition shot didn’t come as a complete surprise. The “crystal ball” that gave them increased pre-shot confidence in a breakthrough involved a combination of detailed...

Visionary report unveils ambitious roadmap to harness the power of AI in scientific discovery

A new report, the product of a series of workshops held in 2022 under the guidance of the U.S. Department of Energy’s Office of Science and the National Nuclear Security Administration, lays out a comprehensive vision for the Office of Science and NNSA to expand their work in scientific use of AI by building on existing strengths in world-leading high performance computing systems and data...