Mechanical Properties Technologist

LLNL, Meta co-develop groundbreaking polymer-chemistry dataset for training AI models. In a pioneering partnership to accelerate materials discovery with AI, researchers from Lawrence Livermore National Laboratory and Meta have created the world's largest open dataset of atomistic polymer chemistry - a trove of millions of quantum-accurate simulations designed to help AI model the complex behavior of plastics, films, batteries and countless everyday materials. (Graphic: Dan Herchek/LLNL; background image: Evan Antoniuk/LLNL) Polymers are fundamental to its daily lives, serving as the core components for a wide array of goods, including clothing, packaging, transportation infrastructure, construction materials and electronics. Advances in polymer science open pathways for recycling and upcycling waste materials into more valuable chemical feedstocks. They also can have an outsized environmental impact: many widely used polymers are Per- and Polyfluoroalkyl Substances (PFAS), widely recognized as "forever chemicals." In a pioneering partnership to accelerate materials discovery with artificial intelligence (AI), researchers from Lawrence Livermore National Laboratory (LLNL) and Meta have created the world's largest open dataset of atomistic polymer chemistry - a trove of millions of quantum-accurate simulations designed to help AI model the complex behavior of plastics, films, batteries and countless everyday materials. In a recent paper, the team details Open Polymers 2026 (OPoly26) - a dataset with an unprecedented number and diversity of polymer structures with corresponding simulations performed at quantum accuracy. OPoly26 is a massive reference library that enables AI to learn patterns from millions of pre-computed polymer structures in hours or days, addressing a longstanding gap in polymer data and laying the foundation for safer, faster and more sustainable materials design. The OPoly26 paper formalizes the dataset's release and demonstrates how the data improves the performance of machine-learned interatomic potentials (MLIPs) on polymer materials. The work builds on the Meta and Lawrence Berkeley National Laboratory (LBNL)-led Open Molecules 2025 (OMol25) Dataset, which is making waves with its sweeping collection of open molecular data aimed at advancing AI-driven chemistry. The OPoly26 dataset contains more than 6 million density functional theory (DFT) calculations on polymeric chemical systems, making it nearly ten times larger than the next largest comparable polymer dataset. LLNL's partnership with Meta - described by LLNL materials scientist and OPoly26 co-principal investigator (PI) Evan Antoniuk as a "natural fit" - seeks to address this shortfall. By generating critical missing data on polymers with the shared goals of expanding and democratizing open datasets for materials scientists, the team hopes to accelerate the pace of discovery across polymer chemistry. "This fills a huge gap," said Antoniuk. "We see this as a community resource, one that we hope becomes the go-to starting point for anyone interested in performing atomistic simulations of polymers." LLNL contributed significant computational power and polymer domain knowledge - generating a diverse set of polymer structures and running simulations to help model how these polymers behave in real-world conditions. In turn, Meta contributed vast computational resources to perform 1.2 billion core hours of DFT simulations and train state-of-the-art MLIP models, leveraging the expertise that had already been refined during their earlier molecular effort. "Meta's partnership with LLNL demonstrates how open science and AI can accelerate breakthroughs in materials research," said Rob Sherman, vice president of policy at Meta. "By making this dataset publicly available, we're giving scientists potent new tools to address critical challenges in healthcare and beyond." LLNL is uniquely positioned to generate the OPoly26 dataset at the scale and fidelity required. Researchers tapped into LLNL's Tuolumne, the world's 12th fastest supercomputer and companion to the exascale El Capitan, leveraging this hardware with their collective expertise to compress years of simulation work into months and enabling the dataset to reach a scale unmatched in polymer science. "Comprehensive coverage of this chemical space is essential to the success of the OPoly26 dataset," said LLNL staff scientist Nick Liesen. "We have worked to leverage pipelines that take us from a simple text string to fully atomistic representations of polymer dynamics at scale." Beyond performing all the DFT calculations, researchers at Meta trained and benchmarked machine-learned interatomic potentials at scale, enabling the team to evaluate how well AI models generalize across small-molecule and polymer chemistry. The paper reports substantial improvements in model accuracy when polymer data is incorporated alongside small-molecule training sets, highlighting the importance of training AI on data that reflects real-world complexity. Understanding why certain polymers, including PFAS-based materials, resist chemical change requires models that can accurately describe both reactive and nonreactive behavior. Capturing this behavior under realistic conditions required careful attention to reactive configurations, according to LBNL chemist and OPoly26 co-PI Sam Blau, who also previously co-led OMol25. "Reactivity - the breakage and formation of chemical bonds - is central to polymer synthesis, manufacturing, aging and recycling, and to nanoscale patterning of polymer thin films for semiconductor manufacturing," said Blau. "By going beyond stable structures and explicitly sampling hundreds of thousands of reactive configurations, we aim to accurately describe the reactive events that often govern polymer behavior under real-world conditions." Beyond outlining how the dataset was generated and performing standard tests of MLIP performance, the OPoly26 paper also introduces an initial suite of polymer-specific evaluation tasks to benchmark how effectively these models capture simulated polymer phenomena and interactions, such as polymer solvation. Future work will include evaluating the MLIP models against experimental measurements, offering a gauge of how well they can capture real-world polymer properties. "LLNL's significant investment in high-performance scientific computing and computational materials science capabilities have been critical to achieving the scale needed to cover many thousands of distinct chemical structures," said LLNL Materials Science Division Leader Ibo Matthews. "That scale is essential not only for generating the data, but for rigorously evaluating how well AI models perform across the full range of polymer behaviors relevant to real-world applications." With a focus on open collaboration, the team is making all data publicly available to fuel polymer advancements across academia, industry and government. The authors also emphasized that OPoly26 is being released under an open license to maximize reuse and reproducibility. Through this open approach, the partnership ensures that the benefits of this public-private investment flow broadly across the entire research community. The team includes LLNL scientists Brian Van Essen, James Diffenderfer, Helgi Ingolfsson and Supun Mohottalalage, and polymer simulation experts Amitesh Maiti and Matt Kroonblawd from the Lab's Materials Science Division. Co-authors also included LBNL's Nitesh Kumar and Lauren Chua. Blau and Kumar's work was funded by the Center for High Precision Patterning Science (CHiPPS), while Chua was supported by her DOE Computational Sciences Graduate Fellowship. LLNL's Laboratory Directed Research and Development program funded the LLNL researchers. This partnership was made possible through a data transfer agreement, facilitated by LLNL's Innovation and Partnerships Office (IPO). IPO is the Laboratory's focal point for industry engagement and facilitates partnerships to deliver mission-driven solutions that support national security and grow the U.S. economy. To connect with LLNL on industrial partnerships in Advanced Computing, AI and Quantum technologies, contact IPO Business Development Executive Clarence Cannon. March 5, 2026 Contact. Jeremy Thomas (925) 422-5539

FY 2027's $1.5T defense budget proposal marks the highest in history! President Donald Trump announced in February on social media he would ask Congress for a $1.5 trillion defense budget for Fiscal Year (FY) 2027-a massive $500 billion increase from this year's Pentagon budget of $1 trillion! The National Defense Authorization Act (NDAA), initially funded defense programs at roughly $850 billion for FY2026. However, this amount later increased to a trillion dollars due to $150 billion in additional funds via the " reconciliation process" - marking the first time defense money was secured through the reconciliation procedure. Nearly $19 billion of those reconciliation funds went to nuclear modernization. Reconciliation is a special Congressional procedure that fast-tracks legislation affecting taxes, spending, and the debt limit, allowing it to pass the Senate with a simple majority (51 votes) rather than the 60 votes required to break a filibuster. It is a two-phase process starting with a budget resolution, followed by committees drafting legislation to meet specific fiscal targets, in this case adding funds to defense programs. For FY 2027, in order to reach Trump's goal of $1.5 trillion, House Armed Services Committee Chairman Mike Rogers (R) is seeking to again add to what is authorized in the NDAA in the upcoming reconciliation bill. The plan? An additional $450 billion in reconciliation funds for defense, three times the $150 billion secured for defense in last year's preliminary reconciliation effort! Apparently, this process is the new normal. Rogers argues that the funding would be necessary to reach the target set by Trump and further stressed that a $1.5 trillion defense budget is necessary to be able to pay for major projects such as the Golden Dome missile shield, sixth-generation F-47 fighter jet, and Sentinel intercontinental ballistic missile (ICBM) program. The recent February 5th expiration of New START, combined with this record budget and major projects, will supercharge a new nuclear arms race. Year after year, Tri Valley Cares is seeing taxpayer burden for the defense budget increase, while crucial services that benefit communities, such as education, health care, and environmental remediation have experienced budget cuts. In 2023, the Pentagon spent far more than China, Russia, and Iran spent on defense combined. Despite the avalanche of funding, nuclear weapons projects continue to be behind schedule and cost more than projected. The Sentinel ICBM, a project the Trump administration claims can't be properly funded without the extra $500 billion increase to the budget, is notorious for its massive cost overruns. In 2024, the project triggered a "Nunn-McCurdy Act breach," resulting in a full review and restructuring of the project. A Nunn-McCurdy breach occurs when a major U.S. defense acquisition program's costs exceed established congressional thresholds, requiring mandatory reporting. The Sentinel ICBM was originally estimated to cost taxpayers $78 billion in 2020, however it is now estimated to cost $141 billion, even with restructuring-an 81% increase! Livermore Lab is the lead lab developing the new W87-1 warhead for the Sentinel ICBM and it continues to receive full funding despite significant delay and failure to even provide a credible cost estimate to Congress (which the National Nuclear Security Administration (NNSA) blames on DOD's delays to the Sentinel missile program.) Due to Tri-Valley CAREs advocacy, Congress demanded that the Government Accountability Office do an investigation into the cost reporting process at NNSA, and it found major problems and offered numerous suggestions. Instead of increasing the budget, Congress needs to be asking the hard questions about where the money is going, and what taxpayers are actually getting, while holding agencies such as the National Nuclear Security Administration and defense contractors accountable for cost overruns. Check out its blog on NNSA cost reporting for more information. More money for these unnecessary and antiquated weapons of a long-bygone era of big nuclear missile fascination is a gross waste of taxpayer dollars. Now is the time for diplomacy and a return to arms control agreements, not the unprecedented expenditure of nuclear weapons. * Post * Share * Copy * Share * Mail * Print * Share

Virgin Galactic partners with Lawrence Livermore National Laboratory to advance high-altitude image-capture technology. ORANGE COUNTY, Calif. - Virgin Galactic (NYSE: SPCE), a leader in commercial spaceflight and advanced aerospace technology, today announced a new collaboration with Lawrence Livermore National Laboratory ("LLNL"), a research and development institution operated for the U.S. Department of Energy. The collaboration will assess potential for utilizing LLNL sensor systems aboard Virgin Galactic launch vehicles in the future, with the aim of gathering critical data and accelerating the development of next-generation image-capture capabilities aboard high-altitude, long-endurance, heavy-lift ("HALE-Heavy") aircraft. "Our launch vehicle has remarkable performance characteristics that can support a variety of high-altitude mission needs" said Michael Colglazier, Chief Executive Officer of Virgin Galactic. "This feasibility study with Lawrence Livermore National Lab is an important step in determining how our vehicle can advance breakthrough technology development in the future." Ben Bahney, LLNL's program leader for space, added: "Our collaboration with Virgin Galactic advances our ability to test and refine our systems in a real-world, high-altitude environment. We are excited to explore the unique combination of altitudes, endurance, and payload capacity of Virgin Galactic's launch vehicles, which could provide unique opportunities to apply and advance LLNL's optical sensing technologies." The Cooperative Research and Development Agreement (CRADA) for the collaboration was facilitated by LLNL's Innovation and Partnerships Office (IPO). IPO is the Laboratory's focal point for industry engagement and facilitates partnerships to deliver mission-driven solutions that support national security and grow the U.S. economy. Virgin Galactic Lawrence Livermore National Laboratory About Virgin Galactic Virgin Galactic is an aerospace and space travel company, pioneering human-first spaceflight for private individuals, researchers, and governments with its advanced SpaceShips and launch vehicle. Scale and profitability are driven by next-generation vehicles capable of taking humans to space at an unprecedented frequency with an industry-leading cost structure. You can find more information at https://www.virgingalactic.com/. About Lawrence Livermore National Laboratory For over 70 years, Lawrence Livermore National Laboratory (LLNL) has applied science and technology to make the world a safer place. The Laboratory's mission is to enable U.S. security and global stability and resilience by empowering multidisciplinary teams to pursue bold and innovative science and technology. This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. We intend such forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 27A of the Securities Act of 1933, as amended (the "Securities Act") and Section 21E of the Securities Exchange Act of 1934, as amended (the "Exchange Act"). All statements contained in this press release other than statements of historical fact, including, without limitation, statements regarding our partnership with LLNL, including plans to install and fly sensor systems on our launch vehicle, plans to gather critical data and accelerate the development of HALE-Heavy aircraft, plans to test the sensor systems, other potential revenue generating applications of our current and future launch vehicles, and potential future collaborations with LLNL are forward-looking statements. The words "believe," "may," "will," "estimate," "potential," "continue," "anticipate," "intend," "expect," "strategy," "future," "could," "would," "project," "plan," "target," and similar expressions are intended to identify forward-looking statements, though not all forward-looking statements use these words or expressions. These statements are neither promises nor guarantees, but involve known and unknown risks, uncertainties and other important factors that may cause our actual results, performance or achievements to be materially different from any future results, performance or achievements expressed or implied by the forward-looking statements, including but not limited to any delay in future commercial flights of our spaceflight fleet, our ability to successfully develop and test our next generation vehicles and their research payload systems, and the time and costs associated with doing so, and the factors, risks and uncertainties included in our Annual Report on Form 10-K for the fiscal year ended December 31, 2024, as such factors may be updated from time to time in our other filings with the Securities and Exchange Commission (the "SEC"), accessible on the SEC's website at www.sec.gov and the Investor Relations section of our website at www.virgingalactic.com, which could cause our actual results to differ materially from those indicated by the forward-looking statements made in this press release. Any such forward-looking statements represent management's estimates as of the date of this press release. While we may elect to update such forward-looking statements at some point in the future, we disclaim any obligation to do so, even if subsequent events cause our views to change.