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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.
    • 2019-05-24 10:05:01
    • Article ID: 713463

    Tapping the power of AI and high-performance computing to extend evolution to superconductors

    • Credit: Argonne National Laboratory/Andreas Glatz

      This image depicts the algorithmic evolution of a defect structure in a superconducting material. Each iteration serves as the basis for a new defect structure. Redder colors indicate a higher current-carrying capacity.

    Materials by design: Argonne researchers use genetic algorithms for better superconductors.

    Owners of thoroughbred stallions carefully breed prizewinning horses over generations to eke out fractions of a second in million-dollar races. Materials scientists have taken a page from that playbook, turning to the power of evolution and artificial selection to develop superconductors that can transmit electric current as efficiently as possible.

    Perhaps counterintuitively, most applied superconductors can operate at high magnetic fields because they contain defects. The number, size, shape and position of the defects within a superconductor work together to enhance the electric current carrying capacity in the presence of a magnetic field. Too many defects, however, can lead to blocking the electric current pathway or a breakdown of the superconducting material, so scientists need to be selective in how they incorporate defects into a material.

    When people think of targeted evolution, they might think of people who breed dogs or horses. Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects.” — Argonne materials scientist Andreas Glatz.

    In a new study from the U.S. Department of Energy’s (DOE) Argonne National Laboratory, researchers used the power of artificial intelligence and high-performance supercomputers to introduce and assess the impact of different configurations of defects on the performance of a superconductor.

    The researchers developed a computer algorithm that treated each defect like a biological gene. Different combinations of defects yielded superconductors able to carry different amounts of current. Once the algorithm identified a particularly advantageous set of defects, it re-initialized with that set of defects as a ​seed,” from which new combinations of defects would emerge.

    Each run of the simulation is equivalent to the formation of a new generation of defects that the algorithm seeks to optimize,” said Argonne distinguished fellow and senior materials scientist Wai-Kwong Kwok, an author of the study. ​Over time, the defect structures become progressively refined, as we intentionally select for defect structures that will allow for materials with the highest critical current.”

    The reason defects form such an essential part of a superconductor lies in their ability to trap and anchor magnetic vortices that form in the presence of a magnetic field. These vortices can move freely within a pure superconducting material when a current is applied. When they do so, they start to generate a resistance, negating the superconducting effect. Keeping vortices pinned, while still allowing current to travel through the material, represents a holy grail for scientists seeking to find ways to transmit electricity without loss in applied superconductors.

    To find the right combination of defects to arrest the motion of the vortices, the researchers initialized their algorithm with defects of random shape and size. While the researchers knew this would be far from the optimal setup, it gave the model a set of neutral initial conditions from which to work. As the researchers ran through successive generations of the model, they saw the initial defects transform into a columnar shape and ultimately a periodic arrangement of planar defects.

    When people think of targeted evolution, they might think of people who breed dogs or horses,” said Argonne materials scientist Andreas Glatz, the corresponding author of the study. ​Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects.”

    One potential drawback to the process of artificial defect selection lies in the fact that certain defect patterns can become entrenched in the model, leading to a kind of calcification of the genetic data. ​In a certain sense, you can kind of think of it like inbreeding,” Kwok said. ​Conserving most information in our defect ​gene pool’ between generations has both benefits and limitations as it does not allow for drastic systemwide transformations. However, our digital ​evolution’ can be repeated with different initial seeds to avoid these problems.”

    In order to run their model, the researchers required high-performance computing facilities at Argonne and Oak Ridge National Laboratory. The Argonne Leadership Computing Facility and Oak Ridge Leadership Computing Facility are both DOE Office of Science User Facilities.

    An article based on the study, ​Targeted evolution of pinning landscapes for large superconducting critical currents,” appeared in the May 21 edition of the Proceedings of the National Academy of Sciences. In addition to Kwok and Glatz, Argonne’s Ivan Sadovskyy, Alexei Koshelev and Ulrich Welp also collaborated.

    Funding for the research came from the DOE’s Office of Science.

    Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

    The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://​ener​gy​.gov/​s​c​ience.

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    AI for Plant Breeding in an Ever-Changing Climate

    AI for Plant Breeding in an Ever-Changing Climate

    In this Q&A, Oak Ridge National Laboratory's Dan Jacobson talks about his team's work on a genomic selection algorithm, his vision for the future of environmental genomics, and the space where simulation meets AI.

    A New Parallel Strategy for Tackling Turbulence on Summit

    A New Parallel Strategy for Tackling Turbulence on Summit

    A team at Georgia Tech created a new turbulence algorithm optimized for the Summit supercomputer. It reached a performance of less than 15 seconds of wall-clock time per time step for more than 6 trillion grid points--a new world record surpassing the prior state of the art in the field for the size of the problem.

    Modeling Every Building in America Starts with Chattanooga

    Modeling Every Building in America Starts with Chattanooga

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    Climate Change Expected to Shift Location of East Asian Monsoons

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    Deep neural networks speed up weather and climate models

    Deep neural networks speed up weather and climate models

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    Scientists Explore Egyptian Mummy Bones With X-Rays and Infrared Light to Gain New Insight on Ancient Life

    Scientists Explore Egyptian Mummy Bones With X-Rays and Infrared Light to Gain New Insight on Ancient Life

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    Etalumis 'Reverses' Simulations to Reveal New Science

    Etalumis 'Reverses' Simulations to Reveal New Science

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    Deep Learning Expands Study of Nuclear Waste Remediation

    Deep Learning Expands Study of Nuclear Waste Remediation

    A research collaboration between Berkeley Lab, Pacific Northwest National Laboratory, Brown University, and NVIDIA has achieved exaflop performance with a deep learning application used to model subsurface flow in the study of nuclear waste remediation


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    ORNL to host 13 teams for DOE CyberForce Competition

    ORNL to host 13 teams for DOE CyberForce Competition

    Oak Ridge National Laboratory will give college students the chance to practice cybersecurity skills in a real-world setting as a host of the Department of Energy's fifth collegiate CyberForce Competition on Nov. 16.

    Argonne nuclear engineer J'Tia Hart selected to Crain's Chicago Business "40 Under 40"

    Argonne nuclear engineer J'Tia Hart selected to Crain's Chicago Business "40 Under 40"

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    Lab-Wide Stormwater Capture, Transportation Savings and Clean-Up Efforts Win Federal Recognition

    Lab-Wide Stormwater Capture, Transportation Savings and Clean-Up Efforts Win Federal Recognition

    Argonne National Laboratory has won a regional Federal Green Challenge award for conserving resources and saving taxpayers' money.

    PPPL wins $70,000 in project funding from DOE for entrepreneurship

    PPPL wins $70,000 in project funding from DOE for entrepreneurship

    The Princeton Plasma Physics Laboratory receives funding from the U.S. Department of Energy for two projects to encourage entrepreneurship and mentor and encourage potential entrepreneurs.

    Brookhaven-Commonwealth Fusion Energy Project Wins DOE Funding

    Brookhaven-Commonwealth Fusion Energy Project Wins DOE Funding

    Brookhaven's Superconducting Magnet Division will partner with industry to develop and characterize superconducting power cables.

    U.S. Department of Energy to Hold Fifth CyberForce Competition(tm)

    U.S. Department of Energy to Hold Fifth CyberForce Competition(tm)

    Going on its fourth year, DOE's CyberForce Competition(tm) on Nov. 15-16 will give teams of cybersecurity students and professionals the opportunity to compete and refine their skills in real-time at 10 national laboratories across the U.S.

    Daniel Gruen awarded 2019 Panofsky Fellowship at SLAC

    Daniel Gruen awarded 2019 Panofsky Fellowship at SLAC

    Daniel Gruen's work on how massive objects bend light from distant galaxies is aimed at unraveling some of the greatest mysteries of modern physics: What is dark matter? What is dark energy, and how is it accelerating the expansion of the universe?

    DOE Announces FY 2020 Small Business Innovation Research Funding Opportunity

    The Department of Energy (DOE) Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs issued its FY 2020 Phase II Release 1 Funding Opportunity Announcement (FOA) with approximately $97 million in available funding.

    Research effort by Argonne National Laboratory and the University of Chicago results in R&D 100 Award

    Research effort by Argonne National Laboratory and the University of Chicago results in R&D 100 Award

    A joint effort by the U.S. Department of Energy's Argonne National Laboratory and the University of Chicago has led to a prestigious R&D 100 Award and is expected to bring an innovation closer to market so it ultimately can be used in many industrial applications.

    Department of Energy Awards Fermilab Funding for Next-Generation Dark Matter Research

    Department of Energy Awards Fermilab Funding for Next-Generation Dark Matter Research

    The U.S. Department of Energy announced that it has awarded scientists at its Fermi National Accelerator Laboratory funding to boost research on dark matter, the mysterious substance that makes up an astounding 85% of the matter in the universe.


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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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