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...

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...

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...

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...

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...

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...

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...

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...

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...

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...