Data Science in the News

Building better materials with data science (VIDEO)

Nov. 11, 2021- 
Research engineer Brian Giera, PhD, describes how data science techniques help collect and analyze data from advanced manufacturing processes in order to craft meaningful experiments. With examples of automated microencapsulation, 3D nanoprinting, metal additive manufacturing, laser track welding, and digital twins, Giera explains how interdisciplinary teams apply machine learning to remove...

Building confidence in materials modeling using statistics

Oct. 31, 2021- 
LLNL statisticians, computational modelers, and materials scientists have been developing a statistical framework for researchers to better assess the relationship between model uncertainties and experimental data. The Livermore-developed statistical framework is intended to assess sources of uncertainty in strength model input, recommend new experiments to reduce those sources of uncertainty...

Inaugural industry forum inspires ML community

Sept. 16, 2021- 
LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center (HPCIC) and Data Science Institute (DSI), the virtual event brought together more than 500 enrollees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia. Industry sponsors included...

LLNL, NNSA and elected officials celebrate opening of Livermore Valley Open Campus expansion

Aug. 26, 2021- 
Leaders from the NNSA, Congressional representatives and local elected officials gathered at LLNL on August 10 to celebrate an expansion to the Livermore Valley Open Campus (LVOC). The Lab hosted a ribbon-cutting ceremony for a new office building (Bldg. 642) and a conference annex (Bldg. 643), which will provide modern office and meeting space for LLNL researchers in predictive biology...

Machine learning aids in materials design

June 10, 2021- 
A long-held goal by chemists across many industries is to imagine the chemical structure of a new molecule and be able to predict how it will function for a desired application. In practice, this vision is difficult, often requiring extensive laboratory work to synthesize, isolate, purify, and characterize newly designed molecules to obtain the desired information. Recently, a team of LLNL...

Advanced Data Analytics for Proliferation Detection shares technical advances during two-day meeting

May 7, 2021- 
The Advanced Data Analytics for Proliferation Detection (ADAPD) program held a two-day virtual technical exchange meeting recently. The goal of the meeting was to highlight the science-based and data-driven analysis work conducted by ADAPD to advance the state-of-the-art to accelerate artificial intelligence (AI) innovation and develop AI-enabled systems to enhance the United States’...

Lab offers forum on machine learning for industry

April 22, 2021- 
LLNL is looking for participants and attendees from industry, research institutions and academia for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10. The event is sponsored by LLNL’s High Performance Computing Innovation Center and the Data Science Institute. The deadline for submitting presentations or industry use cases is June 30. The...

LLNL physicist wins Young Former Student award

Dec. 16, 2020- 
Texas A&M University’s Department of Nuclear Engineering on December 10 announced it has honored LLNL physicist Kelli Humbird with its 2020-21 Young Former Student award for her work at LLNL in combining machine learning with inertial confinement fusion (ICF) research. Humbird graduated from Texas A&M with a PhD in nuclear engineering in 2019. Since joining the Laboratory as an intern in 2016...

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

Nov. 3, 2020- 
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...

The internship that launched a machine-learning target revolution

Oct. 1, 2020- 
Kelli Humbird came to LLNL as a student intern and became a teacher of new data science techniques. In this profile, she describes her experiences and the path that led to her research inertial confinement fusion. Read more at the National Ignition Facility.

Machine learning model may perfect 3D nanoprinting

July 29, 2020- 
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

June 26, 2020- 
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

May 26, 2020- 
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.

Building knowledge and insights using machine learning of scientific articles

May 5, 2020- 
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...

Local Women in Data Science conference showcases Lab research

April 3, 2020- 
For the third consecutive year, LLNL hosted a Women in Data Science (WiDS) regional event on March 2. The event drew dozens of attendees from LLNL, Sandia National Laboratories, local universities, and Bay Area commercial companies. Livermore was one of over 200 regional events in 60 countries coordinated with the main WiDS conference at Stanford University. According to the WiDS website...

Machine learning accelerates high-performance materials development

Feb. 13, 2020- 
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...

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

Nov. 11, 2019- 
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

Nov. 6, 2019- 
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.

Cindy Gonzales forges a new career in data science

Sept. 25, 2019- 
Through LLNL’s Data Science Immersion Program, Gonzales is now among the Lab’s newest data scientists. For two and a half years, she juggled a demanding workload—coordinating Computing’s Scholar Program, interning with data scientists, learning from mentors, supporting LLNL’s Data Science Institute, and attending college part time—while also having her first child. Read more at LLNL Computing...

Collaboration drives data science workshop

Sept. 12, 2019- 
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...