Bulletin of the American Physical Society
2019 Annual Meeting of the APS Four Corners Section
Volume 64, Number 16
Friday–Saturday, October 11–12, 2019; Prescott, Arizona
Session E02: Computational Physics |
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Chair: Christoph Junghans, LANL Room: AC1 113 |
Friday, October 11, 2019 2:48PM - 3:00PM |
E02.00001: Finding representative structures: pathway learning with Path Similarity Analysis-based subsampling Chenou Zhang, Oliver Beckstein Many proteins function as macromolecular machines that switch between different conformational states. In order to understand the molecular mechanisms of such proteins, an important goal is to gain quantitative insights into the conformational transition. Molecular dynamics (MD) simulation have been used to sample such transition pathways but thermal noise from the stochastic nature of MD simulations may obscure the important stages in the transition. Hence finding a series of representative structures that are not affected by thermal fluctuations while retaining all important transition features can be a crucial prerequisite towards any further analysis. Here we demonstrate how to use Path Similarity Analysis (PSA) with the Hausdorff metric to find the most similar “subset path” with a given number of trajectory frames as the best representative subsampled trajectory. We discuss an iterative algorithm that minimizes the Hausdorff path distance under various additional constraints. We analyze the conformational transition pathway of the membrane transporter Mhp1. We show the convergence of our iterative subsampling algorithm and discuss its dependence on initial choices of frames and order parameter. [Preview Abstract] |
Friday, October 11, 2019 3:00PM - 3:12PM |
E02.00002: ABSTRACT WITHDRAWN |
Friday, October 11, 2019 3:12PM - 3:24PM |
E02.00003: (Hyper)Surface Software in Physics Joshua Leiter, Charles Torre Did you know in higher dimensions that you can have more than one normal vector? This makes calculations of geometric properties of surfaces, for example, more involved and more interesting! These geometric properties have many applications in physics. Some examples of these would be: Minimal Surfaces, the 3$+$1 decomposition in general relativity, surface tension, classical string theory, and in geophysical fault isolation. Calculations of these geometric properties can be very difficult, so having software tools to calculate them is desirable. Using the ``DifferentialGeometry'' software I have written a package to calculate geometric properties of submanifolds in any dimension. I have used this package to verify all standard results from classical surface theory and reproduced several results in General Relativity. This software is being used to investigate the construction of interesting examples in hypersurface theory, and to analyze geometric flows such as mean curvature flow. In this talk I will discuss the software and give an explicit example from surface theory and from General Relativity. [Preview Abstract] |
Friday, October 11, 2019 3:24PM - 3:36PM |
E02.00004: Exploring the Behavior Space of the Hodgkin-Huxley Model Joshua Rasband, Mark Transtrum The Hodgkin Huxley model is a set of nonlinear, ordinary differential equations that describes electrical signals in neurons. In their simplest form, the equations include twenty-five parameters that correspond to different physical attributes of the neuron, such as membrane capacitance or conductance of sodium ions. The value of these parameters may vary depending on physical or chemical conditions, such as the structure of an ion channel or the concentration of an ion. There is a vast range of available behaviors that neurons can exhibit depending on the parameters' values. These behaviors are explored over evolutionary timescales by random genetic variation in the parameter values. Evolutionarily favorable behaviors and their corresponding parameters are selected for. We consider the problem of exploring the behavior space of the Hodgkin-Huxley model for different parameter values. As a first step, we have used model reduction techniques to remove irrelevant parameters, i.e., parameters whose variation are unnecessary to explain a specific behavior. We then ask whether the reduced models can accurately mimic behaviors exhibited by the full model. In this way, we begin to map out the behavior space accessible by real neurons. [Preview Abstract] |
Friday, October 11, 2019 3:36PM - 3:48PM |
E02.00005: Parallel Density and Root-Mean-Square Analyses in MDAnalysis Nikolaus Awtrey, Shujie Fan, Oliver Beckstein MDAnalysis is a widely used Python library for the analysis of particle-based simulations in the biomolecular and materials science communities. Parallel MDAnalysis, known as PMDA, is being developed to make use of modern multicore computers as well as high performance supercomputing resources. PMDA uses functions from MDAnalysis along with Dask to seamlessly construct parallel versions of the analyses in MDAnalysis. We added two new analysis tools to PMDA: DensityAnalysis and RMSF. DensityAnalysis gives users the ability to find concentrations of molecules around proteins and other biophysical systems. RMSF calculates the positional root mean square fluctuations of proteins as a measure of their flexibility. We developed a new parallel algorithm for RMSF based on partial means and sums of squares [1]. The results are consistent with the serial version in MDAnalysis and have been verified using the NumPy standard deviation functions on arbitrary data sets. Benchmark results are shown corresponding to typical use. \\{} \\{} [1] T. F. Chan, G. H. Golub, and R. J. LeVeque. In COMPSTAT 1982, Ed. by H. Caussinus, P. Ettinger, and R. Tomassone. (1982), doi: 10.1007/978-3-642-51461-6_3. [Preview Abstract] |
Friday, October 11, 2019 3:48PM - 4:00PM |
E02.00006: Theoretical Studies of Zincate Contamination of $\gamma$ -MnO$_2$ in Deep-Cycled Rechargeable Zn/MnO$_2$ Batteries Nirajan Paudel, Birendra Ale Magar, Timothy Lambert, Igor Vasiliev Rechargeable alkaline Zn/MnO$_2$ batteries are attractive for large-scale energy storage because of their high energy density, non-toxicity, and low cost. However, efforts to develop rechargeable Zn/MnO$_2$ batteries have been hindered by a short cycle life due to the accumulation of irreversible redox reaction products in the $\gamma$-MnO$_2$ cathode. The penetration of Zn ions into the $\gamma$-MnO$_2$ electrode leads to the formation of hetaerolite (ZnMn$_2$O$_4$). The contamination of the $\gamma$-MnO$_2$ cathode material with hetaerolite reduces the battery capacity and eventually leads to the failure of the battery. We apply {\it ab initio} computational methods based on density functional theory to calculate the structure and formation energy of ZnMn$_2$O$_4$ using several different gradient corrected exchange-correlation functionals, including PBE, PBESol, and PBE+U. Our calculations show that the PBE and PBEsol functionals tend to underestimate the formation energy of hetaerolite, whereas the PBE+U functional significantly improves agreement with experiment. Using the results of our calculations, we analyze the influence of hetaerolite on the performance and cycle life of rechargeable Zn/MnO$_2$ batteries. [Preview Abstract] |
Friday, October 11, 2019 4:00PM - 4:12PM |
E02.00007: Solving Linear Differential Equations on a Quantum Computer Scott Johnstun, Jean-Francois Van Huele Problems that reduce to solving linear differential equations are ubiquitous in physics, and quantum computation can be utilized in solving these problems. We present an algorithm and corresponding quantum circuit that allows us to compute the solution to a simple first order system of differential equations, treating the homogeneous and inhomogeneous cases separately. We also present an implementation of our circuits on simulated and real quantum computers and the results obtained. [Preview Abstract] |
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