LLNL’s Computing & Communications portfolio is a gateway for accessing LLNL’s wide variety of solutions and intellectual property for use in information technologies, communications, quantum sciences, data sciences and applied software/modeling & simulations. LLNL’s long history and strong capabilities in computing underpin our success in research, in developing new solutions for our missions, and in our collaborations with the academic and private sectors. We license solutions via diverse mechanisms suited to the use cases, ranging from open-source software licensing, to nonexclusive end user licenses, to custom proprietary licenses for distributors, startups, and other commercialization licensees. We also collaborate with industry partners interested in applying LLNL’s unique capabilities and computing solutions to their company’s challenges.

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An Open-Source, Data-Science Toolkit for Energy Grids

Lawrence Livermore National Laboratory has developed GridDS — an open-source, data-science toolkit for power and data engineers that will provide an integrated energy data storage and augmentation infrastructure, as well as a flexible and comprehensive set of state-of-the-art machine-learning models.

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Transforming the future: LLNL team explores grid modernization via HPC

An ongoing Technology Commercialization Fund (TCF) project with Eaton Corporation is producing great results for grid planning. IPO is proud to present this private sector collaboration at the Innovation XLab Grid Modernization Summit January 24-25.

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Call for HPC for Energy Innovation proposals

DOE's High Performance Computing for Energy Innovation (HPC4EI) Initiative released a call for proposals seeking American companies interested in collaborating with DOE's national laboratories on one-year projects to apply high-performance computing (HPC) modeling, simulation and data analysis to key challenges in U.S. manufacturing and material development.

IT and Communications Technologies

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Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.…

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CRETIN is a 1D, 2D, and 3D non-local thermodynamic equilibrium (NLTE) atomic kinetics/radiation transport code which follows the time evolution of atomic populations and photon distributions as radiation interacts with a plasma consisting of an arbitrary mix of elements. It can provide detailed spectra for comparing with experimental diagnostics.
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LLNL has developed a method of extending device lifetimes by imprinting into the device a shape that excludes specific vibrational modes, otherwise known as a phononic bandgap. Eliminating these modes prevents one of the primary energy loss pathways in these devices. LLNL’s new method enhances the coherence of superconducting circuits by introducing a phononic bandgap around the system’s…

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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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To solve these challenges using new and existing CT system designs, LLNL has developed an innovative software package for CT data processing and reconstruction. Livermore Tomography Tools (LTT) is a modern integrated software package that includes all aspects of CT modeling, simulation, reconstruction, and analysis algorithms based on the latest research in the field. LTT contains the most…

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In order to identify new, unknown proteins associated with viruses, such as COVID-19, it is easiest to start by identifying structurally related proteins. LLNL scientists have created tools that identify structurally related proteins and their relevant residues, called cSpan. The cSpan (sequence conservation in structurally conserved “span” regions) calculation is a quantitative measure of…

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Understanding proteins, their structures, and how they may be similar is necessary for many applications from basic science to developing vaccines for COVID-19. Most computational models that predict protein structure similarity consider certain features at the expense of others. To get a holistic picture of protein structures, LLNL scientists developed the Local-Global Alignment (LGA) model.…

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Automating protein classification via structural similarity has been a technique employeed by researchers for a while. The current methods generally only assess structure similarity using a single metric (e.g., Z-score) and only evaluate similar conformations of secondary structure elements. In order to accurately access structure similarity, LLNL scientists created a method called STRucture…

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Understanding how proteins interact with membrane surfaces is important for drug discovery studies in which a drug may target a membrane protein. One of the main proteins of interest for COVID-19 antibodies is the ACE2 protein that binds to the neutral amino acid transporter B0AT1. B0AT1 sits in the membrane and understanding how movement or perturbation of that membrane might after the…

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Communicating complex scientific information is a critical activity in responding to today’s COVID-19 pandemic. Many sources exist now to present trustworthy and timely information in ways that decisionmakers and the general public can understand. One way to communicate scientific information is to show the technical data in visual forms that users can easily relate to and interact with. LLNL’…

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One of the biggest challenges in many fields of studies, such as COVID-19, is to analyze a complex mix of experimental and simulation data, which relies primarily on the intuition of trained experts. Many advanced analysis techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. To simplify data analysis, LLNL…

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Computed tomography (CT) is one of the most common imaging modalities used in industrial, healthcare, and security settings. During a CT scan, a narrow beam of x-rays is used to produce signals that are processed by a computer to generate cross-sectional images of a part of the human body, such as the lungs of a suspected COVID-19 patient or a patient in recovery needing long-term…

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Cardiotoxicity is one of the major toxicity concerns when developing new drugs. However, these cardiotoxicity tests aren’t done until a drug has gone through years of development. LLNL scientists have developed a software suite called Cardioid that simulates the electrical current running through the heart tissue, triggering cells to contract like cascading dominoes and causing the heart to…

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Protective equipment has always been central to keeping our military, first responders and medical personnel safe. The COVID-19 pandemic is a sobering reminder of the importance of such equipment and the need for improvements. A team lead by LLNL has developed a smart, breathable fabric designed to be incorporated into a garment in small patches to protect the wearer against biological and…

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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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LLNL scientists have designed a rapid PCR technology that incorporates the use of microfluidic thermal heat exchanger systems and is comprised of a porous internal medium, with two outlet channels, two tanks, and one or more exchanger wells for sample receiving. The wells and their corresponding inlet channels are coupled to two tanks that contain fluid with cold and hot temperatures. A…

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LLNL has developed a new technology that provides a method for near-instantaneous heating of aqueous samples in microfluidic devices. The technology relates to a heating method that employs microwave energy absorption from a coincident low power Co-planar waveguide or microwave microstrip transmission line embedded in a microfluidic channel to instantaneously heat samples. The method heat…

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Researchers at LLNL have developed an instantaneous sample heating method to efficiently deposit thermal energy into a continuous stream or segmented microdroplets on a MOEMS device using an optimally low energy, commercially available CO2 laser. The device uses an ideal wavelength (absorption in the far infra-red (FIR) region (λ=10.6 μm)) to instantaneously heat fluidic partitions. The…

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Researchers at LLNL have designed a new technology that allows the integration of a large bench-top thermal cycling instrument onto a miniaturized instrument. This instrument is powered and controlled by portable thumb-drive systems such as an USB. USB thumb-drives are commonly used to transfer data from the instrument onto a PC, however, in this new technology the thumb drive becomes the…

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LLNL researchers have developed a high-volume, low-cost diagnostic test that is easy to use and provides results in under an hour. The testing platform will provide emergency responders and other medical professionals with the ability to screen individuals using oral and nasal samples, and obtain results in approximately 30 minutes. This point-of-care testing approach will enable rapid triage…

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This technology describes a method for performing immediate in-line sample heating to promote the required chemical reactions for amplification, activation, or detection, depending on the thermodynamics of the particular assay involved. The basis of this technology is a method that employ microwave energy absorption to instantaneously heat fluidic partitions without heating the device itself…

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LLNL researchers have developed a new method for faster, more accurate, and precise thermal control for DNA amplification. This technology uses sensor-controlled nodes to monitor and cycle materials through a microfluidic heat exchanging system. Thermal energy travels from a power module through thermal electric elements to sample wells. Sensors coupled to each sample well monitor and respond…

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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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The team’s prototype is intended to be safe, simple and easy to build, while still achieving the minimally required functionality necessary to treat patients with COVID-19. The ventilator has two functional air flow circuits: an inhalation and an exhalation circuit (Figure 1). The pressure in each circuit—Peak Inspiratory Pressure (PIP) and Positive End-Expiratory Pressure (PEEP)—are controlled…
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LLNL researchers have developed a portable device which analyzes one or multiple types of body fluids or gases to test for one or more medical conditions. A bodily fluid (such as blood, perspiration, saliva, breath, or urine) is put into a condenser surface and is then separated into both a primarily gas fluid component and a second one that is primarily liquid. These two samples from the same…

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The LLNL invention has two assay chambers wherein each chamber is comprised of another two chamber modules. This allows the device to process up to two assays per chamber module, or four total assays per biological sample. These two duplex assays are each fed by parallel interrogation ports while the device still maintains a small physical profile. Each port has its own LED for excitation,…

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LLNL scientists have created a standalone pathogen identifier that can be placed in public settings, such as in stores or on street corners. Not unlike an ATM in physical size, this kiosk will accept biological samples from an individual for multiplexed analysis. The sample collection process will be sufficiently simple such that anyone could begin the diagnostic process after making the…

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LLNL researchers have invented a system for identifying all known and unknown pathogenic or non-pathogenic organisms in a sample. This invention takes a complex sample and generates droplets from it. The droplets consist of sub-nanoliter volume reactors which contain the organism sized particles. A lysis device lyses the organisms and releases the nucleic acids. An amplifier then magnifies the…

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LLNL researchers have developed a method to quickly and accurately identify the family of a virus infecting a vertebrate via PCR. Universal primer sets consisting of short nucleic acid strands of 7 to 30 base pairs in length were created to amplify target sequences of viral DNA or RNA. These primers can amplify certain identifying sequences of all viral genomes sequenced to date as well as…

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LLNL scientists have developed a rapid parallel genetic profiling technology that can be used to detect an array of pathogens from a small, complex sample. Detectable pathogens by the LLNL technology include viruses, bacteria, protozoa, and other microbes. The device works by first splitting a given sample into millions of emulsified, encapsulated microdroplets each of which are then split…

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LNLL scientists have invented a method for multiplexed detection of PCR amplified products which can be completed in a single step. Highly validated species-specific primer sets are used to simultaneously amplify multiple diagnostic regions unique to each individual pathogen. Resolution of the mix of amplified products is achieved by PCR product hybridization to corresponding probe sequences,…

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LLNL scientists have developed a technology which fulfills this need. The LLNL technology itself is comprised of two elements which are to be embedded in a user's personal electronic device (e.g. cell phone, tablet device, pager, etc.). The first is a proximity monitor which transmits location and temporal data such as the distance between the user and a contagious individual and the duration…

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This LLNL-developed invention is multiplexed and utilizes the Luminex bead-based liquid array, which contains 100 different unique beads. Oligonucleotide probes with sequences complementary to the target sequences are covalently coupled to these unique beads. These capture beads are mixed with viral samples obtained from the patient via cheek swabbing or a throat wash and subjected to PCR in a…

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The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…

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LLNL has developed a new method for securely processing protected data on HPC systems with minimal impact on the existing HPC operations and execution environment. It can be used with no alterations to traditional HPC operations and can be managed locally. It is fully compatible with traditional (unencrypted) processing and can run other jobs, unencrypted or not, on the cluster simultaneously…

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Simrev is a python library imported into a user-generated program. As the program grows in capability and complexity, the engineered product matures. The "software twin" handles all changes to product configuration and is the portal to running supercomputing analysis and managing workflow for engineering simulation codes. Assemblies become program modules; parts, materials, boundary conditions…

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LLNL has developed a new active memory data reorganization engine. In the simplest case, data can be reorganized within the memory system to present a new view of the data. The new view may be a subset or a rearrangement of the original data. As an example, an array of structures might be more efficiently accessed by a CPU as a structure of arrays. Active memory can assemble an alternative…

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Lawrence Livermore researchers are the first to successfully develop a practical fiber-optic amplifier that generates significant optical gain from 1,390 nanometers (nm) to 1,460 nm with relatively good efficiency. This discovery enables the potential for installed optical fibers to operate in an untapped spectral region known as the E-band, in addition to the C- and L-bands where they…

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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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Antennas are a foundational component of our global communication and information systems. Cell phones, Wi-Fi networks, and satellite links couldn’t exist without them. LLNL scientists, Gerald Burke, Andrew Poggio, and Edward Miller created the Numerical Electromagnetic Code (NEC), an antenna modeling system for wire and surface antennas. As computer capability to handle heavy calculations…
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LLNL’s technology does not use battery-powered tags. Rather it uses a tag technology that has the same range characteristics of battery-powered tags (approximately 10 m) but without the conventional battery.

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The Discriminant Random Forest combines advantages of several methodologies and techniques to produce lower classification error rates.

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The invention utilizes the statistical nature of radiation transport as well as modern processing techniques to implement a physics-based, sequential statistical processor. By this we mean that instead of accumulating a pulse-height spectrum as is done in many other systems, each photon is processed individually upon arrival and then discarded. As each photon arrives, a decision is…

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A ceramic HEPA filter designed to meet commercial and DOE requirements, as well as to minimize upgrade installation logistics for use in existing facilities. Current key performance requirements are described in DOE Standard 3020. The ceramic filter is designed to be nonflammable, corrosion resistant, and compatible with high temperatures and moisture. The ceramic filter will significantly…

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LLNL's new "Catalyst" supercomputer is now available for collaborative projects with American industry. Developed by a partnership with Cray and Intel, the novel architecture behind this high performance computing (HPC) cluster is intended to serve as a proving ground for new HPC and Big Data technologies and algorithms.

Catalyst boasts nearly a terabyte of addressable memory per…