Publications

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Motivated by the disagreement between recent diffusion Monte Carlo calculations of the phase transition pressure between the ambient and beta-Sn phases of silicon and experiments, we present a study of the HCP to BCC phase transition in beryllium. This lighter element provides an opportunity for directly testing many of the approximations required for calculations on silicon and may suggest a path towards increasing the practical accuracy of diffusion Monte Carlo calculations of solids in general. We demonstrate that the single largest approximation in these calculations is the pseudopotential approximation and after removing this we find excellent agreement with experiment for the ambient HCP phase and results similar to careful calculations using density functional theory for the phase transition pressure.

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We use Sandia's Z machine and magnetically accelerated flyer plates to shock compress liquid krypton to 850 GPa and compare with results from density-functional theory (DFT) based simulations using the AM05 functional. We also employ quantum Monte Carlo calculations to motivate the choice of AM05. We conclude that the DFT results are sensitive to the quality of the pseudopotential in terms of scattering properties at high energy/temperature. A new Kr projector augmented wave potential was constructed with improved scattering properties which resulted in excellent agreement with the experimental results to 850 GPa and temperatures above 10 eV (110 kK). Finally, we present comparisons of our data from the Z experiments and DFT calculations to current equation of state models of krypton to determine the best model for high energy-density applications.

We present an improved first-principles description of melting under pressure based on thermodynamic integration comparing density functional theory (DFT) and quantum Monte Carlo (QMC) treatments. The method is applied to address the longstanding discrepancy between DFT calculations and diamond anvil cell (DAC) experiments on the melting curve of xenon, a noble gas solid where van der Waals binding is challenging for traditional DFT methods. The calculations show agreement with data below 20 GPa and that the high-pressure melt curve is well described by a Lindemann behavior up to at least 80 GPa, in contrast to DAC data.

van der Waals forces are notoriously difficult to account for from first principles. We have performed extensive calculations to assess the usefulness and validity of diffusion quantum Monte Carlo when predicting van der Waals forces. We present converged results for noble gas solids and clusters, archetypical van der Waals dominated systems, as well as the highly relevant π-π stacking supramolecular complex: DNA + intercalating anticancer drug ellipticine. Analysis of the calculated binding energies underscores the existence of significant interatomic many-body contributions. © 2014 American Chemical Society.

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In recent years, DFT-MD has been shown to be a useful computational tool for exploring the properties of WDM. These calculations achieve excellent agreement with shock compression experiments, which probe the thermodynamic parameters of the Hugoniot state. New X-ray Thomson Scattering diagnostics promise to deliver independent measurements of electronic density and temperature, as well as structural information in shocked systems. However, they require the development of new levels of theory for computing the associated observables within a DFT framework. The experimentally observable x-ray scattering cross section is related to the electronic density-density response function, which is obtainable using TDDFT - a formally exact extension of conventional DFT that describes electron dynamics and excited states. In order to develop a capability for modeling XRTS data and, more generally, to establish a predictive capability for rst principles simulations of matter in extreme conditions, real-time TDDFT with Ehrenfest dynamics has been implemented in an existing PAW code for DFT-MD calculations. The purpose of this report is to record implementation details and benchmarks as the project advances from software development to delivering novel scienti c results. Results range from tests that establish the accuracy, e ciency, and scalability of our implementation, to calculations that are veri ed against accepted results in the literature. Aside from the primary XRTS goal, we identify other more general areas where this new capability will be useful, including stopping power calculations and electron-ion equilibration.

We apply diffusion quantum Monte Carlo to a broad set of solids, benchmarking the method by comparing bulk structural properties (equilibrium volume and bulk modulus) to experiment and density functional theory (DFT) based theories. The test set includes materials with many different types of binding including ionic, metallic, covalent, and van der Waals. We show that, on average, the accuracy is comparable to or better than that of DFT when using the new generation of functionals, including one hybrid functional and two dispersion corrected functionals. The excellent performance of quantum Monte Carlo on solids is promising for its application to heterogeneous systems and high-pressure/high-density conditions. Important to the results here is the application of a consistent procedure with regards to the several approximations that are made, such as finite-size corrections and pseudopotential approximations. This test set allows for any improvements in these methods to be judged in a systematic way.

This report summarizes the first year’s effort on the Enceladus project, under which Sandia was asked to evaluate the potential advantages of adiabatic quantum computing for analyzing large data sets in the near future, 5-to-10 years from now. We were not specifically evaluating the machine being sold by D-Wave Systems, Inc; we were asked to anticipate what future adiabatic quantum computers might be able to achieve. While realizing that the greatest potential anticipated from quantum computation is still far into the future, a special purpose quantum computing capability, Adiabatic Quantum Optimization (AQO), is under active development and is maturing relatively rapidly; indeed, D-Wave Systems Inc. already offers an AQO device based on superconducting flux qubits. The AQO architecture solves a particular class of problem, namely unconstrained quadratic Boolean optimization. Problems in this class include many interesting and important instances. Because of this, further investigation is warranted into the range of applicability of this class of problem for addressing challenges of analyzing big data sets and the effectiveness of AQO devices to perform specific analyses on big data. Further, it is of interest to also consider the potential effectiveness of anticipated special purpose adiabatic quantum computers (AQCs), in general, for accelerating the analysis of big data sets. The objective of the present investigation is an evaluation of the potential of AQC to benefit analysis of big data problems in the next five to ten years, with our main focus being on AQO because of its relative maturity. We are not specifically assessing the efficacy of the D-Wave computing systems, though we do hope to perform some experimental calculations on that device in the sequel to this project, at least to provide some data to compare with our theoretical estimates.

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