Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session B34: Petascale Science and Beyond: Applications and Opportunities for Materials, Chemical, and Bio Physics IIFocus

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Sponsoring Units: DCOMP DBIO DCP DCMP Chair: Jack Wells, Oak Ridge National Lab Room: LACC 409A 
Monday, March 5, 2018 11:15AM  11:51AM 
B34.00001: Datadriven Molecular Engineering of SolarPowered Windows using Materials Database AutoGeneration Tools with LargeScale DataMining Invited Speaker: Jacqueline Cole Largescale datamining workflows are increasingly able to predict successfully new chemicals that possess a targeted functionality. The success of such materials discovery approaches is nonetheless contingent upon having the right data source to mine, adequate supercomputing facilities and workflows to enable this mining, and algorithms that suitably encode structurefunction relationships as datamining workflows which progressively short list data toward the prediction of a lead material for experimental validation. 
Monday, March 5, 2018 11:51AM  12:03PM 
B34.00002: ALCF Data Science and Machine Learning Programs: From Petascale to Exascale Nichols Romero, Elise Jennings, Álvaro VázquezMayagoitia, Venkatram Vishwanath, Timothy Williams In addition to traditional highperformance computing simulations, ALCF is growing two additional computational science pillars: Data Science and Machine Learning. We anticipate two calls for proposals (CFPs) in 2018. One CFP will be for the ALCF Data Science Program (ADSP) which targets “big data” science problems for current leadership computing resources. Another CFP will be for the ALCF Early Science Program (ESP) targeting the Argonne 2021 exascale supercomputer. The goal of both programs is to explore and improve a variety of computational methods that will enable datadriven discoveries across all scientific disciplines. The projects will focus on data science techniques covering a wide area of discovery including but not limited to uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, realtime data analysis, and complex and interactive workflows. The ESP projects will ultimately have preproduction access to 2021 exascale system to run proposed calculations. The ADSP and ESP CFPs will be announced on the ALCF website, http://www.alcf.anl.gov. 
Monday, March 5, 2018 12:03PM  12:15PM 
B34.00003: Towards RealTime Analysis of Morphologies using Scattering Alexander Hexemer, Ronald Pandolfi, Dinesh Kumar, Harinarayan Krishnan, James Sethian The advent of high brightness sources, fast detectors and the increasing need of timeresolved experiments in small angle scattering has created an unprecedented data deluge and the needs for combining Xray science with computer science. Over the last few years we have worked closely with our computational research and supercomputer division to enable the use of new math, fast networks, supercomputers and new algorithms at beamlines. The goal of such a superfacility would be immediate feedback for scientist during experiments. To streamline the user experience, we have developed a user friendly and plugin based toolkit XICAM that connects different algorithms and allows execution on fast clusters and supercomputers with a push of a button. 
Monday, March 5, 2018 12:15PM  12:27PM 
B34.00004: Atombased polychromatic diffraction pattern predictions for ultrafast timeresolved experiemnts with large scale GPUaccelerated parallel simulation code Juncheng E, Yiyang Zhang, Sen Chen, ShengNian Luo To design and interpret singleshot ultrafast timeresolved synchrotron and XFELbased diffraction experiments, it's desired to simulate the diffraction patterns directly from atomic configurations. GPU accelerated parallel simulation for direct, kinematicsbased, simulations of xray/electron diffraction of largescale atomic systems with mono/polychromatic beams and arbitrary plane detector geometry is implemented here. With this code, nanocrystalline system size can be up to several billion atoms. In the system, nanocrystals can be of arbitrary crystal structures, grain shapes and sizes. Both Xray and electron 2D diffraction patterns can be simulated with arbitrary beam energy in mono or polychromatic beam cases. Intensities in reciprocal space can be directly exploded to help design experiment setups. The application to predict polychromatic diffraction pattern accounting for the polychromatic nature of xray implemented in ultrafast timeresolved synchrotron and XFELbased experiments will presented. 
Monday, March 5, 2018 12:27PM  12:39PM 
B34.00005: New Insights into the Geometric State for TwoFluid Porous Medium Systems James McClure, Ryan Armstrong, Mark Berrill, Steffen Schlüter, Steffen Berg, Cass Miller, William Gray Mechanistic models for twofluid flow in porous medium systems are closed with an empirical relation usually referred to as a capillary pressurefluid saturation relation.The ad hoc and hysteretic nature of this closure relation has been the focus of attention for the last two decades. We show that capillary pressure can be represented as a function of fluid saturation, specific interfacial area, and the Euler characteristic. The posited form of the state equation is investigated by using data generated from synchrotronbased microCT and simulations performed on the Titan supercomputer to explore the possible geometric states for a representative set of media. The results conclusively demonstrate that hysteresis appearing in standard closure relations can be removed and that the resultant state equation describes not only equilibrium states but also dynamic states. This work provides an important foundational component for a new generation of high fidelity multiphase porous medium models. 
Monday, March 5, 2018 12:39PM  12:51PM 
B34.00006: Increased productivity with real time atomistic simulations using Atomify Anders Hafreager, SvennArne Dragly, Anders MaltheSørenssen The typical workflow when running atomistic simulations includes working with several programs. A text editor is needed to create and modify input scripts, the terminal to run the simulation, and programs like VMD or Ovito to visualize the system over time. If physical quantities are computed, the data is often plotted with MATLAB or Python, where additional scripts must be used. This is a tedious process, especially for teaching purposes and for people who are new in the field. I have developed Atomify; a high performance real time visualizer for atomistic simulations. It can simulate and render more than one million atoms with excellent frame rate on modern hardware. Atomify supports OpenMP acceleration, GPU acceleration, real time plotting of physical quantities, and an easytouse code editor in one single application. The software currently uses LAMMPS as physics engine, but it can easily be extended to support other codes like Gromacs, NAMD or OpenMM. Atomify is opensource software (GPL) written in C++ using the Qt framework. 
Monday, March 5, 2018 12:51PM  1:03PM 
B34.00007: Automated Reaction Discovery from Combined Application of Transition State Search Algorithms Colin Grambow, Adeel Jamal, YiPei Li, Judit Zádor, Yury Suleimanov, William Green With petascale computers, new chemistry can be discovered more rapidly in silico than in the laboratory. In this work, we investigate the unimolecular decomposition of γketohydroperoxide using computational schemes that automatically search for elementary reaction steps, i.e., a combination of the Berny optimization algorithm with the freezing string method, the single and doubleended growing string methods, the heuristic KinBot algorithm, and the singlecomponent artificial force induced reaction method (SCAFIR). These efforts lead to a discovery of 75 elementary unimolecular reactions of γketohydroperoxide, 69 of which were previously unknown. All of the methods we adopted found the lowest energy transition state corresponding to the first step of the Korcek mechanism. However, each algorithm except for SCAFIR discovered several reactions not detected by any of the other methods. The present work demonstrates both the strengths and weaknesses of existing schemes for automated reaction discovery and highlights the advantage of the combined application of several computational approaches. However, for the reliable discovery of all important reactions of any given reactants, further method development and assessment is required. 
Monday, March 5, 2018 1:03PM  1:15PM 
B34.00008: Predictive Automated Combustion Chemistry: Massively Parallel HighAccuracy Thermochemistry Murat Keceli, Sarah Elliott, YiPei Li, Matt Johnson, Carlo Cavallotti, Justin Wozniak, Yuri Georgievskii, Ahren Jasper, William Green, Stephen Klippenstein High fidelity mechanisms are crucial for improving the efficiency of combustion devices. We are developing a fuel chemistry code that automates reaction mechanism generation (RMG) with accurate thermochemical kinetics. Accurate computation of thermochemical parameters requires careful treatment of electronic and nuclear degrees of freedom. We couple accurate determinations of the lowest energy torsional conformer with extrapolation of electronic energies to the completebasisset, completecorrelation limit, and detailed examination of anharmonic vibrational effects in predicting the partition functions. To do so, we implemented a Python code, QTC, which automates all aspects of the workflow providing a unified interface for quantum chemistry packages and other codes developed by our team for torsional scans and partition function calculations. Parallelization through Swift scripts allows for very high strong scaling efficiency on supercomputers. Our approach is illustrated by generating a list of important species for butane combustion with RMG followed by largescale automated benchmark thermochemistry calculations with QTC. 
Monday, March 5, 2018 1:15PM  1:27PM 
B34.00009: HighThroughput Phonon Calculations With the RealSpace Multigrid Method (RMG) Jiayong Zhang, Wenchang Lu, Emil Briggs, Yongqiang Cheng, Anibal RamirezCuesta, Jerry Bernholc We have combined our realspace multigrid code (RMG, www.rmgdft.org) with Phonopy to enable phonon calculations on large systems, up to ~1000 atoms, within the framework of Density Functional Theory. RMG is a realspace multigrid method based code for electronicstructure calculations, which is highly parallel with excellent scalability to thousands of nodes and GPUs. Phonopy is an opensource pythonbased package for pre/postprocessing for phonon calculations with interfaces to many DFT codes. Employing the Finite Displacement Method (FDM), we build supercells with small perturbations from atoms’ equilibrium positions and perform selfconsistent calculations to obtain interatomic forces in real space, which are transformed to reciprocal space to form dynamical matrices at arbitrary q values. Several techniques that reduce the computational cost or numerical errors and improve accuracy of the final results will also be discussed. We have used RMG for a variety of systems, four of which will be discussed in detail here: silicon, ZrH2, carbazole and ZIF8. RMG’s results are also compared with inelastic neutron scattering data, measured at the VISION spectrometer at the SNS in ORNL, and other DFT codes for validation purposes. 
Monday, March 5, 2018 1:27PM  1:39PM 
B34.00010: Theory of Band Gaps in Amorphous Nanoporous SiC Tyler Summers, Blair Tuttle, Andrew O'hara, Sokrates Pantelides Nanoporous SiC is an important insulator used as a backendoftheline dielectric in scaled integrated circuits. In the present study, nanoporous SiC atomic models are created from cubic SiC supercells. First, a void of diameter of ~ 1 nm is hydrogen passivated. Then, amorphous models ae constructed with an atom type switching procedure, which was applied until the average atomic composition and bond densities converge to experimental values. Density functional theory calculations are used to explore the electronic and physical properties of various nanoporous SiC models. Specifically, we examine the effect of average atom composition and bonding on the bandgap of nanoporous SiC. 
Monday, March 5, 2018 1:39PM  1:51PM 
B34.00011: Subgroup Discovery for Finding Interpretable Local Patterns in Data from MaterialsScience Bryan Goldsmith, Mario Boley, Christopher Sutton, Jilles Vreeken, Matthias Scheffler, Luca Ghiringhelli We demonstrate that subgroup discovery (SGD) can help find physically meaningful descriptors from materialsscience data obtained by firstprinciples calculations. In contrast to global modelling algorithms, SGD finds descriptions of subpopulations in which, locally, the target property takes on an interesting distribution. First, the SGD algorithm is formulated for materials applications. Next, SGD is applied to gasphase gold clusters (having 5 to 14 atoms) to discern patterns between their geometrical and physicochemical properties. Additionally, SGD is shown to identify subgroups that classify 79 of the 82 octet binary materials as either rock salt or zincblende from only information of its chemical composition. SGD is also used to find descriptors that predict both the formation and bandgap energies of transparent conducting oxides. Lastly, an efficient optimal solver using branchandbound is developed for dispersioncorrected objective functions to help find improved subgroups. 
Monday, March 5, 2018 1:51PM  2:03PM 
B34.00012: Variational Monte Carlo Study of the TwoDimensional Hubbard Model Using SlaterJastrow Wave Functions JeongPil Song, Leonard Sprague, ChiaChen Chang, Brenda Rubenstein We present numerical results for the 2D Hubbard and Extended Hubbard models on a variety of lattices using Variational Monte Carlo with a generalized SlaterJastrow ansatz. We employ stochastic optimization to determine the thousands of Jastrow parameters that parameterize the ground state. In order to fully characterize the ground state physics, we calculate correlation functions for larger lattice sizes than are tractable using projector QMC algorithms and use those results to perform finitesize scaling analyses. Our results support previous findings that the halffilled oneband Hubbard model undergoes a transition to an antiferromagnetic phase at an infinitesimally small value of the critical onsite repulsion. We also discuss the application of our method to ab initio Hamiltonians. 
Monday, March 5, 2018 2:03PM  2:15PM 
B34.00013: Improving efficiency of the continuoustime quantum Monte Carlo Mancheon Han, Hyoung Joon Choi The dynamical mean field theory (DMFT) is a method to investigate strongly correlated materials. This method considers correlations effects in the solid through the Anderson impurity model (AIM). Because there is no analytic solution to AIM, we have to adopt numerical methods called impurity solver. Among various impurity solvers, we implemented continuoustime quantum Monte Carlo approach, which is numerically exact and allow us to deal with any two particle interaction hamiltonian. We tried to improve our impurity solver to calculate the self energy more efficiently and tested its accuracy by comparing it with several model studies. Moreover, we constructed DFT (density functional theory) + DMFT program by combining the developed solver with the SIESTA program. Then we calculated electronic structures of some materials and compared them with experimental data. 
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