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  • richardmitnick 1:49 pm on July 10, 2019 Permalink | Reply
    Tags: , , Atomic force microscopy, Computational materials science, Coupled cluster theory, DFT-density functional theory, Kelvin probe force microscopy, , , , Quantum Monte Carlo (QMC) modeling   

    From Argonne Leadership Computing Facility: “Predicting material properties with quantum Monte Carlo” 

    Argonne Lab
    News from Argonne National Laboratory

    From Argonne Leadership Computing Facility

    July 9, 2019
    Nils Heinonen

    1
    For one of their efforts, the team used diffusion Monte Carlo to compute how doping affects the energetics of nickel oxide. Their simulations revealed the spin density difference between bulks of potassium-doped nickel oxide and pure nickel oxide, showing the effects of substituting a potassium atom (center atom) for a nickel atom on the spin density of the bulk. Credit: Anouar Benali, Olle Heinonen, Joseph A. Insley, and Hyeondeok Shin, Argonne National Laboratory.

    Recent advances in quantum Monte Carlo (QMC) methods have the potential to revolutionize computational materials science, a discipline traditionally driven by density functional theory (DFT). While DFT—an approach that uses quantum-mechanical modeling to examine the electronic structure of complex systems—provides convenience to its practitioners and has unquestionably yielded a great many successes throughout the decades since its formulation, it is not without shortcomings, which have placed a ceiling on the possibilities of materials discovery. QMC is poised to break this ceiling.

    The key challenge is to solve the quantum many-body problem accurately and reliably enough for a given material. QMC solves these problems via stochastic sampling—that is, by using random numbers to sample all possible solutions. The use of stochastic methods allows the full many-body problem to be treated while circumventing large approximations. Compared to traditional methods, they offer extraordinary potential accuracy, strong suitability for high-performance computing, and—with few known sources of systematic error—transparency. For example, QMC satisfies a mathematical principle that allows it to set a bound for a given system’s ground state energy (the lowest-energy, most stable state).

    QMC’s accurate treatment of quantum mechanics is very computationally demanding, necessitating the use of leadership-class computational resources and thus limiting its application. Access to the computing systems at the Argonne Leadership Computing Facility (ALCF) and the Oak Ridge Leadership Computing Facility (OLCF)—U.S. Department of Energy (DOE) Office of Science User Facilities—has enabled a team of researchers led by Paul Kent of Oak Ridge National Laboratory (ORNL) to meet the steep demands posed by QMC. Supported by DOE’s Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program, the team’s goal is to simulate promising materials that elude DFT’s investigative and predictive powers.

    To conduct their work, the researchers employ QMCPACK, an open-source QMC code developed by the team. It is written specifically for high-performance computers and runs on all the DOE machines. It has been run at the ALCF since 2011.

    Functional materials

    The team’s efforts are focused on studies of materials combining transition metal elements with oxygen. Many of these transition metal oxides are functional materials that have striking and useful properties. Small perturbations in the make-up or structure of these materials can cause them to switch from metallic to insulating, and greatly change their magnetic properties and ability to host and transport other atoms. Such attributes make the materials useful for technological applications while posing fundamental scientific questions about how these properties arise.

    The computational challenge has been to simulate the materials with sufficient accuracy: the materials’ properties are sensitive to small changes due to complex quantum mechanical interactions, which make them very difficult to model.

    The computational performance and large memory of the ALCF’s Theta system have been particularly helpful to the team. Theta’s storage capacity has enabled studies of material changes caused by small perturbations such as additional elements or vacancies. Over three years the team developed a new technique to more efficiently store the quantum mechanical wavefunctions used by QMC, greatly increasing the range of materials that could be run on Theta.

    ANL ALCF Theta Cray XC40 supercomputer

    Experimental Validation

    Kent noted that experimental validation is a key component of the INCITE project. “The team is leveraging facilities located at Argonne and Oak Ridge National Laboratories to grow high-quality thin films of transition-metal oxides,” he said, including vanadium oxide (VO2) and variants of nickel oxide (NiO) that have been modified with other compounds.

    For VO2, the team combined atomic force microscopy, Kelvin probe force microscopy, and time-of-flight secondary ion mass spectroscopy on VO2 grown at ORNL’s Center for Nanophase Materials Science (CNMS) to demonstrate how oxygen vacancies suppress the transition from metallic to insulating VO2. A combination of QMC, dynamical mean field theory, and DFT modeling was deployed to identify the mechanism by which this phenomenon occurs: oxygen vacancies leave positively charged holes that are localized around the vacancy site and end up distorting the structure of certain vanadium orbitals.

    For NiO, the challenge was to understand how a small quantity of dopant atoms, in this case potassium, modifies the structure and optical properties. Molecular beam epitaxy at Argonne’s Materials Science Division was used to create high quality films that were then probed via techniques such as x-ray scattering and x-ray absorption spectroscopy at Argonne’s Advanced Photon Source (APS) [below] for direct comparison with computational results. These experimental results were subsequently compared against computational models employing QMC and DFT. The APS and CNMS are DOE Office of Science User Facilities.

    So far the team has been able to compute, understand, and experimentally validate how the band gap of materials containing a single transition metal element varies with composition. Band gaps determine a material’s usefulness as a semiconductor—a substance that can alternately conduct or cease the flow of electricity (which is important for building electronic sensors or devices). The next steps of the study will be to tackle more complex materials, with additional elements and more subtle magnetic properties. While more challenging, these materials could lead to greater discoveries.

    New chemistry applications

    Many of the features that make QMC attractive for materials also make it attractive for chemistry applications. An outside colleague—quantum chemist Kieron Burke of the University of California, Irvine—provided the impetus for a paper published in Journal of Chemical Theory and Computation. Burke approached the team’s collaborators with a problem he had encountered while trying to formulate a new method for DFT. Moving forward with his attempt required benchmarks against which to test his method’s accuracy. As QMC was the only means by which sufficiently precise benchmarks could be obtained, the team produced a series of calculations for him.

    The reputed gold standard for many-body system numerical techniques in quantum chemistry is known as coupled cluster theory. While it is extremely accurate for many molecules, some are so strongly correlated quantum-mechanically that they can be thought of as existing in a superposition of quantum states. The conventional coupled cluster method cannot handle something so complicated. Co-principal investigator Anouar Benali, a computational scientist at the ALCF and Argonne’s Computational Sciences Division, spent some three years collaborating on efforts to expand QMC’s capability so as to include both low-cost and highly efficient support for these states that will in future also be needed for materials problems. Performing analysis on the system for which Burke needed benchmarks required this superposition support; he verified the results of his newly developed DFT approach against the calculations generated with Benali’s QMC expansion. They were in close agreement with each other, but not with the results conventional coupled cluster had generated—which, for one particular compound, contained significant errors.

    “This collaboration and its results have therefore identified a potential new area of research for the team and QMC,” Kent said. “That is, tackling challenging quantum chemical problems.”

    The research was supported by DOE’s Office of Science. ALCF and OLCF computing time and resources were allocated through the INCITE program.

    See the full article here .

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    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. For more visit http://www.anl.gov.

    About ALCF
    The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community.

    We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and expertise.

    ALCF projects cover many scientific disciplines, ranging from chemistry and biology to physics and materials science. Examples include modeling and simulation efforts to:

    Discover new materials for batteries
    Predict the impacts of global climate change
    Unravel the origins of the universe
    Develop renewable energy technologies

    Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science

    Argonne Lab Campus


     
  • richardmitnick 5:58 pm on October 17, 2018 Permalink | Reply
    Tags: , , Quantum Monte Carlo (QMC) modeling, Quantum predictions   

    From ASCR Discovery: “Quantum predictions” 

    From ASCR Discovery
    ASCR – Advancing Science Through Computing

    1
    Mechanical strain, pressure or temperature changes or adding chemical doping agents can prompt an abrupt switch from insulator to conductor in materials such as nickel oxide (pictured here). Nickel ions (blue) and oxygen ions (red) surround a dopant ion of potassium (yellow). Quantum Monte Carlo methods can accurately predict regions where charge density (purple) will accumulate in these materials. Image courtesy of Anouar Benali, Argonne National Laboratory.

    Solving a complex problem quickly requires careful tradeoffs – and simulating the behavior of materials is no exception. To get answers that predict molecular workings feasibly, scientists must swap in mathematical approximations that speed computation at accuracy’s expense.

    But magnetism, electrical conductivity and other properties can be quite delicate, says Paul R.C. Kent of the Department of Energy’s (DOE’s) Oak Ridge National Laboratory. These properties depend on quantum mechanics, the movements and interactions of myriad electrons and atoms that form materials and determine their properties. Researchers who study such features must model large groups of atoms and molecules rather than just a few. This problem’s complexity demands boosting computational tools’ efficiency and accuracy.

    That’s where a method called quantum Monte Carlo (QMC) modeling comes in. Many other techniques approximate electrons’ behavior as an overall average, for example, rather than considering them individually. QMC enables accounting for the individual behavior of all of the electrons without major approximations, reducing systematic errors in simulations and producing reliable results, Kent says.

    Kent’s interest in QMC dates back to his Ph.D. research at Cambridge University in the 1990s. At ORNL, he recently returned to the method because advances in both supercomputer hardware and in algorithms had allowed researchers to improve its accuracy.

    “We can do new materials and a wider fraction of elements across the periodic table,” Kent says. “More importantly, we can start to do some of the materials and properties where the more approximate methods that we use day to day are just unreliable.”

    Even with these advances, simulations of these types of materials, ones that include up to a few hundred atoms and thousands of electrons, requires computational heavy lifting. Kent leads a DOE Basic Energy Sciences Center, the Center for Predictive Simulations of Functional Materials (CPSFM) that includes researchers from ORNL, Argonne National Laboratory, Sandia National Laboratories, Lawrence Livermore National Laboratory, the University of California, Berkeley and North Carolina State University.

    Their work is supported by a DOE Innovative and Novel Computational Impact on Theory and Experiments (INCITE) allocation of 140 million processor hours, split between Oak Ridge Leadership Computing Facility’s Titan and Argonne Leadership Computing Facility’s Mira supercomputers. Both computing centers are DOE Office of Science user facilities.

    ORNL Cray Titan XK7 Supercomputer

    MIRA IBM Blue Gene Q supercomputer at the Argonne Leadership Computing Facility

    To take QMC to the next level, Kent and colleagues start with materials such as vanadium dioxide that display unusual electronic behavior. At cooler temperatures, this material insulates against the flow of electricity. But at just above room temperature, vanadium dioxide abruptly changes its structure and behavior.

    Suddenly this material becomes metallic and conducts electricity efficiently. Scientists still don’t understand exactly how and why this occurs. Factors such as mechanical strain, pressure or doping the materials with other elements also induce this rapid transition from insulator to conductor.

    However, if scientists and engineers could control this behavior, these materials could be used as switches, sensors or, possibly, the basis for new electronic devices. “This big change in conductivity of a material is the type of thing we’d like to be able to predict reliably,” Kent says.

    Laboratory researchers also are studying these insulator-to-conductors with experiments. That validation effort lends confidence to the predictive power of their computational methods in a range of materials. The team has built open-source software, known as QMCPACK, that is now available online and on all of the DOE Office of Science computational facilities.

    Kent and his colleagues hope to build up to high-temperature superconductors and other complex and mysterious materials. Although scientists know these materials’ broad properties, Kent says, “we can’t relate those to the actual structure and the elements in the materials yet. So that’s a really grand challenge for the condensed-matter physics field.”

    The most accurate quantum mechanical modeling methods restrict scientists to examining just a few atoms or molecules. When scientists want to study larger systems, the computation costs rapidly become unwieldy. QMC offers a compromise: a calculation’s size increases cubically relative to the number of electrons, a more manageable challenge. QMC incorporates only a few controlled approximations and can be applied to the numerous atoms and electrons needed. It’s well suited for today’s petascale supercomputers – capable of one quadrillion calculations or more each second – and tomorrow’s exascale supercomputers, which will be at least a thousand times faster. The method maps simulation elements relatively easily onto the compute nodes in these systems.

    The CPSFM team continues to optimize QMCPACK for ever-faster supercomputers, including OLCF’s Summit, which will be fully operational in January 2019.

    ORNL IBM AC922 SUMMIT supercomputer. Credit: Carlos Jones, Oak Ridge National Laboratory/U.S. Dept. of Energy

    The higher memory capacity on that machine’s Nvidia Volta GPUs – 16 gigabytes per graphics processing unit compared with 6 gigabytes on Titan – already boosts computation speed. With the help of OLCF’s Ed D’Azevedo and Andreas Tillack, the researchers have implemented improved algorithms that can double the speed of their larger calculations.

    QMCPACK is part of DOE’s Exascale Computing Project, and the team is already anticipating additional scaling challenges for running QMCPACK on future machines. To perform the desired simulations within roughly 12 hours on an exascale supercomputer, Kent estimates that they’ll need algorithms that are 30 times more scalable than those within the current version.

    Depiction of ANL ALCF Cray Shasta Aurora exascale supercomputer

    Even with improved hardware and algorithms, QMC calculations will always be expensive. So Kent and his team would like to use QMCPACK to understand where cheaper methods go wrong so that they can improve them. Then they can save QMC calculations for the most challenging problems in materials science, Kent says. “Ideally we will learn what’s causing these materials to be very tricky to model and then improve cheaper approaches so that we can do much wider scans of different materials.”

    The combination of improved QMC methods and a suite of computationally cheaper modeling approaches could lead the way to new materials and an understanding of their properties. Designing and testing new compounds in the laboratory is expensive, Kent says. Scientists could save valuable time and resources if they could first predict the behavior of novel materials in a simulation.

    Plus, he notes, reliable computational methods could help scientists understand properties and processes that depend on individual atoms that are extremely difficult to observe using experiments. “That’s a place where there’s a lot of interest in going after the fundamental science, predicting new materials and enabling technological applications.”

    Oak Ridge National Laboratory is supported by the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.

    See the full article here.


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    ASCRDiscovery is a publication of The U.S. Department of Energy

     
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