Data Science in the News

DOE announces five new energy projects at LLNL

The DOE today announced two rounds of awards for the High Performance Computing for Energy Innovation Program HPC4EI), including five projects at LLNL. HPC4EI connects industry with the computational resources and expertise of the DOE national laboratories to solve challenges in manufacturing, accelerate discovery and adoption of new materials and improve energy efficiency. The awards were...

From intern to mentor, Nisha Mulakken builds a career in bioinformatics

The COVID-19 pandemic has sparked a wave of new research and development at the Lab, and Nisha Mulakken is very busy. The biostatistician has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated...

Advancing healthcare with data science (VIDEO)

This video provides an overview of projects in which data scientists work with domain scientists to address major challenges in healthcare. To help fight the COVID-19 pandemic, researchers are developing computer models to search for potential antibody and antiviral drug treatments, sharing a data portal with scientists and the general public, and analyzing drug compounds via a novel text...

Machine learning model may perfect 3D nanoprinting

Two-photon lithography (TPL)—a widely used 3D nanoprinting technique that uses laser light to create 3D objects—has shown promise in research applications but has yet to achieve widespread industry acceptance due to limitations on large-scale part production and time-intensive setup. LLNL scientists and collaborators turned to machine learning to address two key barriers to industrialization...

Lockdown doesn’t hinder annual Data Science Challenge

Due to the COVID-19 pandemic and shelter-in-place restrictions, this year’s Data Science Challenge with the University of California, Merced was an all-virtual offering. The two-week challenge involved 21 UC Merced students who worked from their homes through video conferencing and chat programs to develop machine learning models capable of differentiating potentially explosive materials from...

AI identifies change in microstructure in aging materials

LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Read more at LLNL News.

COVID-19 research goes public through new portal

A new online data portal is making available to the public a wealth of data LLNL scientists have gathered from their ongoing COVID-19 molecular design projects, particularly the computer-based “virtual” screening of small molecules and designed antibodies for interactions with the SARS-CoV-2 virus for drug design purposes. The portal houses a wealth of data LLNL scientists have gathered from...

Building knowledge and insights using machine learning of scientific articles

Nanomaterials are widely used at LLNL and in industry for many applications from catalysis to optics to additive manufacturing. The combination of nanomaterials’ shape, size, and composition can impart unique optical, electrical, mechanical, or catalytic properties needed for a specific application. However, synthesizing a specific nanomaterial and scaling up its production is often...

LLNL creates web resources to aid in fight against COVID-19

LLNL is fully committed to helping protect the U.S. from COVID-19 and to speed the recovery of those affected. As a world-class research institute, we have considerable infrastructure, unique research capabilities and a dedicated team of scientists and engineers supporting the fight against the COVID-19 pandemic. Our current COVID-19 research and response activities are focused on four broad...

Machine learning accelerates high-performance materials development

Lawrence Livermore National Laboratory (LLNL) and its partners rely on timely development and deployment of diverse materials to support a variety of national security missions. However, materials development and deployment can take many years from initial discovery of a new material to deployment at scale. Now, an interdisciplinary team of LLNL researchers from the Physical and Life Sciences...

Deep learning may provide solution for efficient charging, driving of autonomous electric vehicles

LLNL computer scientists and software engineers have developed a deep learning-based strategy to maximize electric vehicle (EV) ride-sharing services while reducing carbon emissions and the impact to the electrical grid, emphasizing autonomous EVs capable of offering 24-hour service. Read more at LLNL News.

Department of Energy researchers share data management strategies at first-ever “Data Day”

It’s become something of a mantra of the digital age: Data is the new currency. Especially in science, where it’s hard to find a single project that doesn’t involve generating or consuming massive amounts of data. In light of the growing awareness of the critical importance of data management across the Department of Energy complex, more than 100 researchers from DOE national laboratories...

Big data illuminates the physical sciences

Livermore teams are applying innovative data analysis and interpretation techniques to advance fundamental science research. This article describes projects in astrophysics and materials science. Read more at Science & Technology Review.

Lab leads effort to model proteins tied to cancer

Computational scientists, biophysicists and statisticians from LLNL and Los Alamos National Laboratory(LANL) are leading a massive multi-institutional collaboration that has developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancer. Read more at LLNL News.

Successful simulation and visualization coupling proves the power of Sierra

As the first National Nuclear Security Administration (NNSA) production supercomputer backed by GPU- (graphics processing unit) accelerated architecture, Sierra’s acquisition required a fundamental shift in how scientists at Lawrence Livermore National Laboratory (LLNL) program their codes to take advantage of the GPUs. The majority of Sierra’s computational power—95 percent of its 125...

Collaboration drives data science workshop

Lawrence Livermore National Laboratory’s (LLNL’s) Data Science Institute (DSI) hosted its second annual workshop on July 23–24, 2019. Co-sponsored by the University of California (UC) system, the event drew more than 200 participants to Garré Winery in Livermore. A common theme ran throughout both days: Collaboration is always welcome. Indeed, feedback from last year’s workshop inspired a...

NFL comes to Lab to hear latest on TBI research

Officials from the National Football League visited LLNL to hear how the Department of Energy’s national laboratories are using high-performance computing and artificial intelligence to advance scientific understanding of traumatic brain injury (TBI). Read more at LLNL News.

Researchers explore machine learning to automate sorting of microcapsules in real-time

Micro-Encapsulated CO2 Sorbents (MECS) — tiny, reusable capsules full of a sodium carbonate solution that can absorb carbon dioxide from the air — are a promising technology for capturing carbon from the atmosphere. To create the caviar-like objects, scientists run three fluids through a series of microfluidic components to create drops that turn into capsules when exposed to ultraviolet...

Multi-institutional meeting centers on making the 'impossible' possible in cancer research

Computer scientists, biomedical engineers, cancer biologists and bioinformaticians from eight Department of Energy national laboratories, health-related government agencies and universities converged at LLNL March 6-7 to discuss ongoing efforts to advance cancer research through computation, identify existing challenges and brainstorm future multidisciplinary projects. Read more at LLNL News...

CASC research showcased at major data science venues

Researchers from LLNL’s Center for Applied Scientific Computing (CASC) are among the Lab’s employees making waves in the data science community, with many prominent accolades, publications, and acceptances in 2018. Data science encompasses some of the hottest technology topics—machine learning (ML), “big data” analysis, artificial intelligence, computer vision, and more—and the Center’s...