Data Science in the News

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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...

For better CT images, new deep learning tool helps fill in the blanks

Nov. 17, 2023 - 
At a hospital, an airport, or even an assembly line, computed tomography (CT) allows us to investigate the otherwise inaccessible interiors of objects without laying a finger on them. To perform CT, x-rays first shine through an object, interacting with the different materials and structures inside. Then, the x-rays emerge on the other side, casting a projection of their interactions onto a...

LLNL’s Kailkhura elevated to IEEE senior member

Nov. 8, 2023 - 
IEEE, the world’s largest technical professional organization, has elevated LLNL research staff member Bhavya Kailkhura to the grade of senior member within the organization. IEEE has more than 427,000 members in more than 190 countries, including engineers, scientists and allied professionals in the electrical and computer sciences, engineering and related disciplines. Just 10% of IEEE’s...

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...

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...

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...

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...

Cognitive simulation supercharges scientific research

Jan. 10, 2023 - 
Computer modeling has been essential to scientific research for more than half a century—since the advent of computers sufficiently powerful to handle modeling’s computational load. Models simulate natural phenomena to aid scientists in understanding their underlying principles. Yet, while the most complex models running on supercomputers may contain millions of lines of code and generate...

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...

LLNL researchers win HPCwire award for applying cognitive simulation to ICF

Nov. 17, 2022 - 
The high performance computing publication HPCwire announced LLNL as the winner of its Editor’s Choice award for Best Use of HPC in Energy for applying cognitive simulation (CogSim) methods to inertial confinement fusion (ICF) research. The award was presented at the largest supercomputing conference in the world: the 2022 International Conference for High Performance Computing, Networking...

LLNL team claims top AI award at international symbolic regression competition

Aug. 16, 2022 - 
An LLNL team claimed a top prize at an inaugural international symbolic regression competition for an artificial intelligence (AI) framework they developed capable of explaining and interpreting real-life COVID-19 data. Hosted by the open source SRBench project at the 2022 Genetic and Evolutionary Computation Conference (GECCO), the competition had two tracks—synthetic and real-world—and...

Introduction to deep learning for image classification workshop (VIDEO)

July 6, 2022 - 
In addition to its annual conference held every March, the global Women in Data Science (WiDS) organization hosts workshops and other activities year-round to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field. On June 29, LLNL’s Cindy Gonzales led a WiDS Workshop titled “Introduction to Deep Learning for Image Classification.” The abstract...

Assured and robust…or bust

June 30, 2022 - 
The consequences of a machine learning (ML) error that presents irrelevant advertisements to a group of social media users may seem relatively minor. However, this opacity, combined with the fact that ML systems are nascent and imperfect, makes trusting their accuracy difficult in mission-critical situations, such as recognizing life-or-death risks to military personnel or advancing materials...

CASC team wins best paper at visualization symposium

May 25, 2022 - 
A research team from LLNL’s Center for Applied Scientific Computing won Best Paper at the 15th IEEE Pacific Visualization Symposium (PacificVis), which was held virtually on April 11–14. Computer scientists Harsh Bhatia, Peer-Timo Bremer, and Peter Lindstrom collaborated with University of Utah colleagues Duong Hoang, Nate Morrical, and Valerio Pascucci on “AMM: Adaptive Multilinear Meshes.”...

Paving the way to tailor-made carbon nanomaterials and more accurate energetic materials modeling

March 17, 2022 - 
To better understand how carbon nanomaterials could be tailor-made and how their formation impacts shock phenomena such as detonation, LLNL scientists conducted machine-learning-driven atomistic simulations to provide insight into the fundamental processes controlling the formation of nanocarbon materials, which could serve as a design tool, help guide experimental efforts and enable more...

Unprecedented multiscale model of protein behavior linked to cancer-causing mutations

Jan. 10, 2022 - 
LLNL researchers and a multi-institutional team have developed a highly detailed, machine learning–backed multiscale model revealing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers. Published by the Proceedings of the National Academy of Sciences, the paper details the methodology behind the Multiscale Machine...

Understanding materials behavior with data science (VIDEO)

Dec. 21, 2021 - 
Computational chemist Rebecca Lindsey, PhD, explains how machine learning and data science techniques are used to develop diagnostic tools for stockpile stewardship, such as models that predict detonator performance. Lindsey also describes how atomistic simulations improve researchers’ understanding of the microscopic phenomena that govern the chemistry in materials under extreme conditions...

LLNL establishes AI Innovation Incubator to advance artificial intelligence for applied science

Dec. 20, 2021 - 
LLNL has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts in artificial intelligence (AI) from LLNL, industry and academia to advance AI for large-scale scientific and commercial applications. LLNL has entered into a new memoranda of understanding with Google, IBM and NVIDIA, with plans to use the incubator to facilitate discussions and form future...