Bulletin of the American Physical Society
Annual Meeting of the APS Four Corners Section
Volume 62, Number 17
Friday–Saturday, October 20–21, 2017; Fort Collins, CO
Session L3: Theoretical and Computational Condensed Matter II |
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Chair: Vladimiro Mujica, Arizona State University Room: Lory Student Center 382 |
Saturday, October 21, 2017 11:10AM - 11:34AM |
L3.00001: Nanoscale Terraced Topographies Produced by Ion Bombardment of Solid Surfaces Invited Speaker: R. Mark Bradley Bombarding a solid surface with an obliquely-incident, broad ion beam can lead to the emergence of surface ripples with wavelengths as short as 10 nanometers. The anisotropic Kuramoto-Sivashinsky (AKS) equation has been used to model the formation of these ripples for more than two decades. However, when the angle of incidence is large, intriguing phenomena are observed that are not reproduced by the AKS equation. We have introduced an equation of motion for the surface of an ion-bombarded material that differs from the AKS equation by the inclusion of a cubic nonlinearity. This additional nonlinear term results from an improved approximation to the sputter yield. We have shown that this term can have a crucial influence on the dynamics --- it can lead to the formation of a terraced topography that coarsens in time, in accord with experimental observations for high incidence angles. Our simulations also establish that regular terraced surfaces produced by bombarding a surface with a sinusoidal pre-pattern could serve as highly efficient blazed diffraction gratings.\\ \\This research was done in collaboration with D. A. Pearson and M. P. Harrison [Preview Abstract] |
Saturday, October 21, 2017 11:34AM - 11:46AM |
L3.00002: Using Machine Learning to Accurately Predict Ambient Soundscapes Katrina Pedersen, Brooks Butler, Kent Gee, Mark Transtrum The ability to accurately characterize the soundscape, or combination of sounds, of various areas is valuable to the United States military and the National Park Service. It also holds weight in areas such as epidemiology and ecology. We attempt to use machine learning to correctly predict ambient sound levels throughout the contiguous United States. Our data set consists of roughly 500 training sites, where various acoustic metrics, such as overall daytime L50 levels and frequency band levels, have been obtained. We also have roughly 115 geospatial features, which include distances to the nearest roads or airports, and the percentage of industrialization or forest in a specific area. Selecting useful and physically significant features is an important step in the process of creating a good model. Feature importance measures, such as the Gini importance, help guide feature reduction. Ideally, the feature importance rankings will have physical significance and vary depending on which acoustic metric we have created the model for. We discuss the results and process of feature reduction on our model. [Preview Abstract] |
Saturday, October 21, 2017 11:46AM - 11:58AM |
L3.00003: First principles study of the BeF$_{\mathrm{\mathbf{2}}}$\textbf{ phase diagram: A critical comparison of outcomes using LDA, GGA and van der Waals corrected DFT } Andrew Chizmeshya, George Wolf, Narges Masoumi Beryllium fluoride continues to draw attention because of its industrial and scientific applications, and because of its structural analogy with silica, but its detailed phase diagram has yet to be established experimentally. Recently the ordering of a dozen BeF$_{\mathrm{2}}$ crystalline polymorphs was studied using GGA-based DFT static lattice enthalpies (Nelson et al, PRB \textbf{95} 054118, 2017). For pressures below 8 GPa this approach predicts the phase sequence [$\alpha $-cristobalite $\to \alpha $-quartz$\to $coesite$\to $moganite] while severely overestimating the volume of the ambient pressure $\alpha $-quartz phase by \textasciitilde 10{\%}. Since lattice expansion arising from thermal corrections would further increase this error we undertook a critical reinvestigation of the structures, relative stabilities and compression behavior of candidate phases using thermally corrected LDA and van der Waals corrected (DFT-D3) functionals. We find that the static lattice LDA description correctly predicts the observed $\alpha $-quartz ground state, and a phase ordering [$\alpha $-quartz$\to $coesite$\to $moganite] and that inclusion of thermal corrections at 300K do not significantly alter this ordering. In contrast, while the dispersion corrected PBE-GGA functional (DFT-D3) reproduces the observed 300K equilibrium volume only in the static lattice approximation, thermal corrections worsen the agreement. A more severe deficiency of DFT-D3 is that compressibility is severely underestimated (by about 50{\%}) making it unsuitable for EOS studies. A rudimentary model of the BeF$_{\mathrm{2}}$ phase diagram based on quasi-harmonic free-energy (LDA) will be discussed in detail. [Preview Abstract] |
Saturday, October 21, 2017 11:58AM - 12:10PM |
L3.00004: Modeling Splitting in Flocking Boids Using Directed Graphs Christian Lambert, Manuel Berrondo We present a dynamic model where a flock of simulated birds (boids) exists in two dimensions. Each boid has a constant speed and a fixed randomly determined number of neighbors defined as those boids that influence the direction of its motion (consensus). The flock of boids is then mapped to a directed graph. Rigorously defined sub-flocks are identified using graph theory and assigned different colors. Modifications of the graph following a specific algorithm (frustration) during the simulation results in emergent behavior. Animations will be shown to demonstrate the emergent behavior. Statistics of the sub-flocks are gathered and presented. This graph analysis can expand our understanding of how and when dynamic emergence occurs in this flocking model. Graph terminology and concepts will be explained as a part of the presentation. [Preview Abstract] |
Saturday, October 21, 2017 12:10PM - 12:22PM |
L3.00005: TORQUE, a Software Package to Predict Water Orientations in Ionic Crystals. Seyedayat Ghazisaeed, Boris Kiefer It is well known that H2O can affect phase stability and materials performance. For example, water content in air can affect efficiencies of organic-inorganic perovskite solar cell. Thus, the experimental identification of water orientations is highly desirable but remains challenging due to the small scattering cross section of hydrogen. Similarly, first-principles computations depend strongly on unit cell size and available computing resources. We have developed a Linux based software package based on rotational equilibrium and point charge electrostatics for predicting the orientation of crystallization water molecules in ionic crystals. This method is at least \textasciitilde 300 times faster than first-principles density-functional-theory (DFT) computations and provides optimized orientations that are consistent with experiment and theory. Interestingly, it provides a new H2O orientation in Kernite crystal that has not been reported previously. Our DFT computations show that the two conformations are energetically in-equivalent. Thus, the torque method provides a new, simple, robust, and fast method to complete initial structures for ab-initio computations of ionic materials that contain crystallization water and provide initial water orientations for experimental structure refinements. [Preview Abstract] |
Saturday, October 21, 2017 12:22PM - 12:34PM |
L3.00006: Why do calculations of metals converge so slowly? Gus Hart, Jeremy Jorgensen, Wiley Morgan, Hayden Oliver, Parker Hamilton Calculating the energy of a metal from ``first principles'' requires a three-dimensional integration of a multivalued function over an irregular domain (the ``Fermi surface''). The numerical convergence of this integral is not only extremely slow but is also very irregular. We will give simple examples demonstrating the source of the difficulty and explain what strategies might be effective to combat the problem. We will show data on real materials and discuss why this problem is so central to computational materials science. [Preview Abstract] |
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