DOE News
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    The DOE Science News Source is a Newswise initiative to promote research news from the Office of Science of the DOE to the public and news media.
    • 2019-11-12 10:50:04
    • Article ID: 722408

    Deep Learning Expands Study of Nuclear Waste Remediation

    • A schematic of the physics-informed generative adversarial network used to estimate parameters and quantify uncertainty in the subsurface flow at the Hanford Site.

    A research collaboration between Lawrence Berkeley National Laboratory (Berkeley Lab), Pacific Northwest National Laboratory (PNNL), Brown University, and NVIDIA has achieved exaflop performance on the Summit supercomputer with a deep learning application used to model subsurface flow in the study of nuclear waste remediation. Their achievement, which will be presented during the “Deep Learning on Supercomputers” workshop at SC19, demonstrates the promise of physics-informed generative adversarial networks (GANs) for analyzing complex, large-scale science problems.

    “In science we know the laws of physics and observation principles – mass, momentum, energy, etc.,” said George Karniadakis, professor of applied mathematics at Brown and co-author on the SC19 workshop paper. “The concept of physics-informed GANs is to encode prior information from the physics into the neural network. This allows you to go well beyond the training domain, which is very important in applications where the conditions can change.”

    GANs have been applied to model human face appearance with remarkable accuracy, noted Prabhat, a co-author on the SC19 paper who leads the Data and Analytics Services team at Berkeley Lab’s National Energy Research Scientific Computing Center. “In science, Berkeley Lab has explored the application of vanilla GANs for creating synthetic universes and particle physics experiments; one of the open challenges thus far has been the incorporation of physical constraints into the predictions,” he said. “George and his group at Brown have pioneered the approach of incorporating physics into GANs and using them to synthesize data – in this case, subsurface flow fields.”

    For this study, the researchers focused on the Hanford Site, established in 1943 as part of the Manhattan Project to produce plutonium for nuclear weapons and eventually home to the first full-scale plutonium production reactor in the world, eight other nuclear reactors, and five plutonium-processing complexes. When plutonium production ended in 1989, left behind were tens of millions of gallons of radioactive and chemical waste in large underground tanks and more than 100 square miles of contaminated groundwater resulting from the disposal of an estimated 450 billion gallons of liquids to soil disposal sites. So for the past 30 years the U.S. Department of Energy has been working with the Environmental Protection Agency and the Washington State Department of Ecology to clean up Hanford, which is located on 580 square miles (nearly 500,000 acres) in south-central Washington, whole parts of it adjacent to the Columbia River - the largest river in the Pacific Northwest and a critical thoroughfare for industry and wildlife.

    To track the cleanup effort, workers have relied on drilling wells at the Hanford Site and placing sensors in those wells to collect data about geologic properties and groundwater flow and observe the progression of contaminants. Subsurface environments like the Hanford Site are very heterogeneous with varying in space properties, explained Alex Tartakovsky, a computational mathematician at PNNL and co-author on the SC19 paper. “Estimating the Hanford Site properties from data only would require more than a million measurements, and in practice we have maybe a thousand. The laws of physics help us compensate for the lack of data."

    “The standard parameter estimation approach is to assume that the parameters can take many different forms, and then for each form you have to solve subsurface flow equations perhaps millions of times to determine parameters best fitting the observations,” Tartakovsky added. But for this study the research team took a different tack: using a physics-informed GAN and high performance computing to estimate parameters and quantify uncertainty in the subsurface flow.  

    For this early validation work, the researchers opted to use synthetic data - data generated by a computed model based on expert knowledge about the Hanford Site. This enabled them to create a virtual representation of the site that they could then manipulate as needed based on the parameters they were interested in measuring - primarily hydraulic conductivity and hydraulic head, both key to modeling the location of the contaminants. Future studies will incorporate real sensor data and real-world conditions.

    “The initial purpose of this project was to estimate the accuracy of the methods, so we used synthetic data instead of real measurements,” Tartakovsky said. “This allowed us to estimate the performance of the physics-informed GANS as a function of the number of measurements.”

    In training the GAN on the Summit supercomputer at the Oak Ridge Leadership Computing Facility OLCF, the team was able to achieve 1.2 exaflop peak and sustained performance – the first example of a large-scale GAN architecture applied to SPDEs. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site required training the GAN model to thousands of dimensions, so the team developed a highly optimized implementation that scaled to 27,504 NVIDIA V100 Tensor Core GPUs and 4,584 nodes on Summit with a 93.1% scaling efficiency.

    “Achieving such a massive scale and performance required full stack optimization and multiple strategies to extract maximum parallelism,” said Mike Houston, who leads the AI Systems team at NVIDIA. “At the chip level, we optimized the structure and design of the neural network to maximize Tensor Core utilization via cuDNN support in TensorFlow. At the node level, we used NCCL and NVLink for high-speed data exchange. And at the system level, we optimized Horovod and MPI not only to combine the data and models but to handle adversary parallel strategies. To maximize utilization of our GPUs, we had to shard the data and then distribute it to align with the parallelization technique.”

    “This is a new high-water mark for GAN architectures,” Prabhat said. “We wanted to create an inexpensive surrogate for a very costly simulation, and what we were able to show here is that a physics-constrained GAN architecture can produce spatial fields consistent with our knowledge of physics. In addition, this exemplar project brought together experts from subsurface modeling, applied mathematics, deep learning, and HPC. As the DOE considers broader applications of deep learning - and, in particular, GANs - to simulation problems, I expect multiple research teams to be inspired by these results.”

    This research is supported in part by the DOE’s Center for Physics Informed Learning Machines (PhILMs), a collaboration between PNNL and Sandia Laboratory, with academic partners at Brown University, Massachusetts Institute of Technology, Stanford University, and the University of California, Santa Barbara.

    The paper, “Highly Scalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs,” will be presented at the SC19 Deep Learning on Supercomputers workshop. In addition to Karniadakis, Prabhat, Tartakovsky, and Houston, co-authors are Thorsten Kurth of NERSC, L. Yang of Brown University, David Barajas-Solano of PNNL, Sean Treichler and Joshua Romero of NVIDIA, and Keno Fischer and Valentin Churavy of Julia Computing.

    NERSC and OLCF are DOE Office of Science user facilities.

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    Current model for storing nuclear waste is incomplete

    Current model for storing nuclear waste is incomplete

    The materials the United States and other countries plan to use to store high-level nuclear waste will likely degrade faster than anyone previously knew because of the way those materials interact, new research shows. The findings, published today in the journal Nature Materials, show that corrosion of nuclear waste storage materials accelerates because of changes in the chemistry of the nuclear waste solution, and because of the way the materials interact with one another.

    Here, There and Everywhere: Large and Giant Viruses Abound Globally

    Here, There and Everywhere: Large and Giant Viruses Abound Globally

    In Nature, a team led by U.S. Department of Energy (DOE) Joint Genome Institute (JGI) researchers uncovered a broad diversity of large and giant viruses that belong to the nucleocytoplasmic large DNA viruses (NCLDV) supergroup, expanding virus diversity in this group 10-fold from just 205 genomes.

    Chemistry finding could make solar energy more efficient

    Chemistry finding could make solar energy more efficient

    Scientists for the first time have developed a single molecule that can absorb sunlight efficiently and also act as a catalyst to transform solar energy into hydrogen, a clean alternative to fuel for things like gas-powered vehicles. This new molecule collects energy from the entire visible spectrum, and can harness more than 50% more solar energy than current solar cells can. The finding could help humans transition away from fossil fuels and toward energy sources that do not contribute to climate change.

    New model helps pave the way to bringing clean fusion energy down to Earth

    New model helps pave the way to bringing clean fusion energy down to Earth

    State-of-the-art simulation confirms a key source of heat and energy loss in spherical fusion facilities.

    Rising global temperatures turn northern permafrost region into significant carbon source

    Rising global temperatures turn northern permafrost region into significant carbon source

    A new study that incorporates datasets gathered from more than 100 sites by institutions including the U.S. Department of Energy's (DOE) Argonne National Laboratory, suggests that decomposition of organic matter in permafrost soil is substantially larger than previously thought, demonstrating the significant impact that emissions from the permafrost soil could have on the greenhouse effect and global warming.

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    Transformative 'Green' Accelerator Achieves World's First 8-pass Full Energy Recovery

    Scientists from Cornell University and Brookhaven National Laboratory (BNL) have successfully demonstrated the world's first capture and reuse of energy in a multi-turn particle accelerator, where electrons are accelerated and decelerated in multiple stages and transported at different energies through a single beamline.

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    First detailed electronic study of new nickelate superconductor finds 3D metallic state

    It represents an entirely new type of ground state for transition metal oxides, and opens new directions for experiments and theoretical studies of how superconductivity arises and how it can be optimized in this system and possibly in other compounds.

    What's MER? It's a Way to Measure Quantum Materials, and It's Telling Us New and Interesting Things

    What's MER? It's a Way to Measure Quantum Materials, and It's Telling Us New and Interesting Things

    Experimental physicists have combined several measurements of quantum materials into one in their ongoing quest to learn more about manipulating and controlling the behavior of them for possible applications. They even coined a term for it-- Magneto-elastoresistance, or MER.

    Scientists pioneer new generation of semiconductor neutron detector

    Scientists pioneer new generation of semiconductor neutron detector

    In a new study, scientists have developed a new type of semiconductor neutron detector that boosts detection rates by reducing the number of steps involved in neutron capture and transduction.

    Connecting the dots in the sky could shed new light on dark matter

    Connecting the dots in the sky could shed new light on dark matter

    Astrophysicists have come a step closer to understanding the origin of a faint glow of gamma rays covering the night sky. They found that this light is brighter in regions that contain a lot of matter and dimmer where matter is sparser - a correlation that could help them narrow down the properties of exotic astrophysical objects and invisible dark matter.


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    Lin Chen receives Award in Experimental Physical Chemistry

    Lin Chen receives Award in Experimental Physical Chemistry

    The Physical Chemistry Division of the American Chemical Society announces that Lin X. Chen has received the 2020 Award in Experimental Physical Chemistry. The award recognizes Chen for "fundamental contributions to the elucidation of excited state structures, dynamics and energetics of light harvesting systems.

    Polymer expert Advincula named ORNL-UT Governor's Chair

    Polymer expert Advincula named ORNL-UT Governor's Chair

    Rigoberto "Gobet" Advincula has been named Governor's Chair of Advanced and Nanostructured Materials at the Department of Energy's Oak Ridge National Laboratory and the University of Tennessee.

    Former PPPL intern honored for outstanding machine learning poster

    Former PPPL intern honored for outstanding machine learning poster

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    Team led by PPPL wins major supercomputer time to help capture on Earth the fusion that powers the sun and stars

    Team led by PPPL wins major supercomputer time to help capture on Earth the fusion that powers the sun and stars

    PPPL will use INCITE-award time on Summit and Theta supercomputers to develop predictions for the performance of ITER, the international experiment under construction to demonstrate the feasibility of fusion energy.

    Department of Energy Announces $625 Million for New Quantum Centers

    The U.S. Department of Energy (DOE) announced up to $625 million over the next five years to establish two to five multidisciplinary Quantum Information Science (QIS) Research Centers in support of the National Quantum Initiative.

    Department of Energy to Provide $75 Million for Bioenergy Crops Research

    The U.S. Department of Energy (DOE) announced a plan to provide up to $75 million over five years for research to develop sustainable bioenergy crops tolerant of environmental stress and resilient to changing environmental conditions.

    Jefferson Lab to be Major Partner in Electron Ion Collider Project

    Jefferson Lab to be Major Partner in Electron Ion Collider Project

    The Department of Energy announced that it has taken the next step toward construction of an Electron Ion Collider (EIC) in the United States. DOE announced on Thursday that the collider will be sited at DOE's Brookhaven National Laboratory in Upton, N.Y. In addition, DOE's Thomas Jefferson National Accelerator Facility will be a major partner in realizing the EIC, providing key support to build this next new collider, which will be the most advanced particle collider of its type ever built.

    Department of Energy Selects Site for Electron-Ion Collider

    Department of Energy Selects Site for Electron-Ion Collider

    UPTON, NY-- Yesterday, the U.S. Department of Energy (DOE) named Brookhaven National Laboratory on Long Island in New York as the site for building an Electron-Ion Collider (EIC), a one-of-a-kind nuclear physics research facility. This announcement, following DOE's approval of "mission need" (known as Critical Decision 0) on December 19, 2019, enables work to begin on R&D and the conceptual design for this next-generation collider at Brookhaven Lab.

    Department of Energy Announces $32 Million for Small Business Research and Development Grants

    U.S. Secretary of Energy Dan Brouillette announced that the Department of Energy (DOE) will award 158 grants totaling $32 million to 118 small businesses in 32 states. Funded through DOE's Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, these selections are for Phase I research and development.

    Summit Charts a Course to Uncover the Origins of Genetic Diseases

    Summit Charts a Course to Uncover the Origins of Genetic Diseases

    Gene mutations can interfere with how the body expresses genes and cause disease. To better understand this connection, researchers recently developed a model of the transcription preinitiation complex (PIC).


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    Harvesting Energy from Light using Bio-inspired Artificial Cells

    Harvesting Energy from Light using Bio-inspired Artificial Cells

    Scientists designed and connected two different artificial cells to each other to produce molecules called ATP (adenosine triphosphate).

    Engineering Living Scaffolds for Building Materials

    Engineering Living Scaffolds for Building Materials

    Bone and mollusk shells are composite systems that combine living cells and inorganic components. This allows them to regenerate and change structure while also being very strong and durable. Borrowing from this amazing complexity, researchers have been exploring a new class of materials called engineered living materials (ELMs).

    Excavating Quantum Information Buried in Noise

    Excavating Quantum Information Buried in Noise

    Researchers developed two new methods to assess and remove error in how scientists measure quantum systems. By reducing quantum "noise" - uncertainty inherent to quantum processes - these new methods improve accuracy and precision.

    How Electrons Move in a Catastrophe

    How Electrons Move in a Catastrophe

    Lanthanum strontium manganite (LSMO) is a widely applicable material, from magnetic tunnel junctions to solid oxide fuel cells. However, when it gets thin, its behavior changes for the worse. The reason why was not known. Now, using two theoretical methods, a team determined what happens.

    When Ions and Molecules Cluster

    When Ions and Molecules Cluster

    How an ion behaves when isolated within an analytical instrument can differ from how it behaves in the environment. Now, Xue-Bin Wang at Pacific Northwest National Laboratory devised a way to bring ions and molecules together in clusters to better discover their properties and predict their behavior.

    Tune in to Tetrahedral Superstructures

    Tune in to Tetrahedral Superstructures

    Shape affects how the particles fit together and, in turn, the resulting material. For the first time, a team observed the self-assembly of nanoparticles with tetrahedral shapes.

    Tracing Interstellar Dust Back to the Solar System's Formation

    Tracing Interstellar Dust Back to the Solar System's Formation

    This study is the first to confirm dust particles pre-dating the formation of our solar system. Further study of these materials will enable a deeper understanding of the processes that formed and have since altered them.

    Investigating Materials that Can Go the Distance in Fusion Reactors

    Investigating Materials that Can Go the Distance in Fusion Reactors

    Future fusion reactors will require materials that can withstand extreme operating conditions, including being bombarded by high-energy neutrons at high temperatures. Scientists recently irradiated titanium diboride (TiB2) in the High Flux Isotope Reactor (HFIR) to better understand the effects of fusion neutrons on performance.

    Better 3-D Imaging of Tumors in the Breast with Less Radiation

    Better 3-D Imaging of Tumors in the Breast with Less Radiation

    In breast cancer screening, an imaging technique based on nuclear medicine is currently being used as a successful secondary screening tool alongside mammography to improve the accuracy of the diagnosis. Now, a team is hoping to improve this imaging technique.

    Microbes are Metabolic Specialists

    Microbes are Metabolic Specialists

    Scientists can use genetic information to measure if microbes in the environment can perform specific ecological roles. Researchers recently analyzed the genomes of over 6,000 microbial species.


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