The National Security mission at the lab supports advanced technology needs of the nation. We support some of the advanced needs for the Departments of Defense, Homeland Security, Justice, State, EPA as well as international partners and state governments. LLNL excels in programs for High Explosives, Sensors, Space missions, Materials, Intelligence, Forensic Sciences, High Performance Simulation and Computing. The LLNL facilities have some of the largest research labs in the nation spread over several thousand acres.

Portfolio News and Multimedia

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LLNL and Partners Leveraging Microorganisms to Separate and Purify Rare-Earth Elements

LLNL, Penn State, Columbia University, Tufts University, University of Kentucky, Purdue University and industry partner Western Rare Earths will use microbial and biomolecular engineering to develop a scalable bio-based separation and purification strategy for rare-earth elements

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Three LLNL Scientists Inducted into LLNL’s Entrepreneurs’ Hall of Fame

A trio of LLNL scientists have been inducted into the laboratory's Entrepreneur's Hall of Fame. Each developed technologies during or after their Lab careers that created major economic impacts or spawned new companies.

National Security and Defense Technologies

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LLNL is seeking industry partners to collaborate on quantum science and technology research and development in the following areas: quantum-coherent device physics, quantum materials, quantum–classical interfaces, computing and simulation, and sensing and detection.

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Analyzing the performance and efficiency of complex facilities with modern instrumented components – and the performance of regional networks of such facilities - is a daunting task. Increasingly, facilities collect data from manually input systems as well as diverse Internet of Things sensors and monitoring tools for specialty equipment, storage systems, computing networks, and power/cooling…

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Complex problems, such as COVID-19, are being studying computational, prior to be tested experimentally. These complex computational problems require HPC resources, of which must be understood and allocated properly. This requires the user to waste valuable computational time just setting up a job on the HPC system. In order to allow computational scientists to focus on the science, LLNL…

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To understand complex problems using machine learning it is generally necessary to have large amounts of data. In order to generate these large amounts of data, researchers utilize simulation. Simulations are best run on High-Performance Computers (HPCs) which require various complex processes. To simplify running machine learning based workflows on HPCs, LLNL scientists developed Merlin. The…

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LLNL has a successful history of developing instruments for detecting and characterizing airborne pathogens. Often, aerosol characterizing instruments require highly focused particle beams with little or no transmission losses. In addition, they need to interface to the sampling environment with a very high sampling rate so that more aerosol particles can be collected and sensitivity can be…

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In 2005, LLNL researchers won a R&D 100 award for developing advanced technologies to rapidly detect the airborne release of biological threat agents. The Biological Aerosol Mass Spectrometry (BAMS) system is an instrument about the size of three podiums that can analyze individual aerosol particles in real time and at high rates to almost instantly identify the presence and concentration…

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Natural Language Processing (NLP) is a field of study which aims to program computers to process and analyze large amount of natural language data. In order to accurately and effectively utilize datasets in NLP systems, labeled datasets are a must. In cases like pathology reports, the sub-parts of the report are not programmatically labeled. To solve the unlabeled dataset problem, LLNL…

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Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, LLNL scientists developed function preserving projections (FPP), a…

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Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…

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Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…

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Drug discovery could be significantly sped up by the integration of in silico methods. To this end, LLNL scientists along with other ATOM Consortium members created the ATOM Modeling PipeLine (AMPL). AMPL is an open-source, modular, extensible, end-to-end software pipeline for building and sharing models. It extends the functionality of DeepChem and supports an array of machine learning and…

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When analyzing a dataset, one must not only understand the relationship between the data points, but also the underlying structure of the set. The underlying structure of a dataset is generally estimated from the data on hand, leading to assumptions and less accurate predictions. In order to improve structure learning, LLNL scientists have developed an open source software suite called MTL.…

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MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…

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Livermore Lab researchers have developed a tunable shaped charge which comprises a cylindrical liner commonly a metal such as copper or molybdenum but almost any solid material can be used and a surround layer of explosive in which the detonation front is constrained to propagate at an angle with respect to the charge axis.  The key to the concept is the ability to deposit a…

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Livermore Lab researchers have developed a method that combines additive manufacturing (AM) with an infill step to render a final component which is energetic. In this case, AM is first used to print a part of the system, and this material can either be inert or energetic on its own. A second material is subsequently added to the structure via a second technique such as casting, melt…

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This technology uses AM printing methods applied to explosives materials. But unlike producing explosives parts, the explosive component is added at a low concentration of around 4 to 6 wt. %. This allows for the final form, to be labeled as a non-hazardous material. A suitable matrix (substrate) is selected that ultimately will be non-volatile (reducing improper training on contaminants) and…
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3D printing involves the layer-by-layer deposition of one, or more, materials. The spatial placement of the material, if carefully controlled, can influence a desired static or dynamic property. The use of 3D printing to build complex and unique energetic components is at the center of LLNL’s architected energetic materials and structures effort. LLNL has developed several different methods…

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LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…

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LLNL's high fidelity hydrocode is capable of predicting blast loads and directly coupling those loads to structures to predict a mechanical response. By combining this code and our expertise in modeling blast-structure interaction and damage, along with our access to experimental data and testing facilities, we can contribute to the design of protective equipment that can better mitigate the…

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LLNL has developed a wide band (WB) ground penetrating radar (GPR) technology to detect and image buried objects under a moving vehicle. Efficient and high performance processing algorithms reconstruct images of buried or hidden objects in two or three dimensions under a scanning array. The technology includes a mobile high-performance computing system allowing GPR array sensor data to be…

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The HERMES bridge inspector is an ultrawideband-based nondestructive evaluation (NDE) system. The LLNL-developed system provides 3-D ground penetrating radar information. An array of micropower impulse radar (MIR) sensors is mounted under a trailer. Reflected radar data is gathered by driving the trailer over a bridge at 55 mph and 3-D image maps of the internal structure of the bridge deck…