EikoNet: Solving the Eikonal equation with Deep Neural NetworksThe recent deep learning revolution has created an enormous opportunity for accelerating compute capabilities in the context of physics-based simulations. Here, we propose EikoNet, a deep learning…Jonathan D. Smith, Kamyar Azizzadenesheli, Zachary E. Ross·Mar 25, 2020SaveLearn
Performance of Julia for High Energy Physics AnalysesWe argue that the Julia programming language is a compelling alternative to implementations in Python and C++ for common data analysis workflows in high energy physics. We compare the speed of…Marcel Stanitzki, Jan Strube·Mar 24, 2020SaveLearn
Machine Learning for Quantum MatterQuantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science,…Juan Carrasquilla·Mar 24, 2020SaveLearn
Lattice Boltzmann simulations capture the multiscale physics of soft flowing crystalsThe study of the underlying physics of soft flowing materials depends heavily on numerical simulations, due to the complex structure of the governing equations reflecting the competition of…Andrea Montessori, Adriano Tiribocchi, Fabio Bonaccorso et al.·Mar 24, 2020SaveLearn
Portably parallel construction of a CI wave function from a matrix-product state using the Charm++ frameworkThe constructions of configuration interaction (CI) expansions from a matrix-product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations when in a…Ting Wang, Yingjin Ma, Lian Zhao et al.·Mar 24, 2020SaveLearn
Direct Numerical Simulations of turbulent flows using high-order Asynchrony-Tolerant schemes: accuracy and performanceDirect numerical simulations (DNS) are an indispensable tool for understanding the fundamental physics of turbulent flows. Because of their steep increase in computational cost with Reynolds number…Komal Kumari, Diego A. Donzis·Mar 23, 2020SaveLearn
Dynamic load balancing with enhanced shared-memory parallelism for particle-in-cell codesFurthering our understanding of many of today's interesting problems in plasma physics---including plasma based acceleration and magnetic reconnection with pair production due to quantum…Kyle G. Miller, Roman P. Lee, Adam Tableman et al.·Mar 23, 2020SaveLearn
Validation of pseudopotential calculations for the electronic band gap of solidsNowadays pseudopotential density-functional theory calculations constitute the standard approach to tackle solid-state electronic problems. These rely on distributed pseudopotential tables that were…Pedro Borlido, Jan Doumont, Fabien Tran et al.·Mar 23, 2020SaveLearn
Asymptotic approximations for Bloch waves and topological mode steering in a planar array of Neumann scatterersWe study the canonical problem of wave scattering by periodic arrays, either of infinite or finite extent, of Neumann scatterers in the plane; the characteristic lengthscale of the scatterers is…Richard Wiltshaw, Richard V. Craster, Mehul P. Makwana·Mar 22, 2020SaveLearn
Mode-space-compatible inelastic scattering in atomistic nonequilibrium Green's function implementationsThe nonequilibrium Green's function (NEGF) method is often used to predict transport in atomistically resolved nanodevices and yields an immense numerical load when inelastic scattering on…Daniel A. Lemus, James Charles, Tillmann Kubis·Mar 21, 2020SaveLearn
Simulating disordered quantum systems via dense and sparse restricted Boltzmann machinesIn recent years, generative artificial neural networks based on restricted Boltzmann machines (RBMs) have been successfully employed as accurate and flexible variational wave functions for clean…S. Pilati, P. Pieri·Mar 21, 2020SaveLearn
Parallel 3d shape optimization for cellular composites on large distributed-memory clustersSkin modeling is an ongoing research area that highly benefits from modern parallel algorithms. This article aims at applying shape optimization to compute cell size and arrangement for elastic…Jose Pinzon, Martin Siebenborn, Andreas Vogel·Mar 21, 2020SaveLearn
BetheSF: Efficient computation of the exact tagged-particle propagator in single-file systems via the Bethe eigenspectrumSingle-file diffusion is a paradigm for strongly correlated classical stochastic many-body dynamics and has widespread applications in soft condensed matter and biophysics. However, exact results for…Alessio Lapolla, Aljaz Godec·Mar 20, 2020SaveLearn
A numerical approach for fluid deformable surfacesFluid deformable surfaces show a solid-fluid duality which establishes a tight interplay between tangential flow and surface deformation. We derive the governing equations as a thin film limit and…Sebastian Reuther, Ingo Nitschke, Axel Voigt·Mar 20, 2020SaveLearn
Systematic errors in diffusion coefficients from long-time molecular dynamics simulations at constant pressureIn molecular dynamics simulations under periodic boundary conditions, particle positions are typically wrapped into a reference box. For diffusion coefficient calculations using the Einstein…Sören von Bülow, Jakob Tómas Bullerjahn, Gerhard Hummer·Mar 20, 2020SaveLearn
Optimal estimates of diffusion coefficients from molecular dynamics simulationsTranslational diffusion coefficients are routinely estimated from molecular dynamics simulations. Linear fits to mean squared displacement (MSD) curves have become the de facto standard, from simple…Jakob Tómas Bullerjahn, Sören von Bülow, Gerhard Hummer·Mar 20, 2020SaveLearn
Accelerating Auxiliary-Field Quantum Monte Carlo Simulations of Solids with Graphical Processing UnitWe outline how auxiliary-field quantum Monte Carlo (AFQMC) can leverage graphical processing units (GPUs) to accelerate the simulation of solid state sytems. By exploiting conservation of crystal…Fionn D. Malone, Shuai Zhang, Miguel A. Morales·Mar 20, 2020SaveLearn
Discontinuity-resolving shock-capturing schemes on unstructured gridsSolving compressible flows containing discontinuities remains a major challenge for numerical methods especially on unstructured grids. Thus in this work, we make contributions to shock capturing…Lidong Cheng, Xi Deng, Bin Xie et al.·Mar 20, 2020SaveLearn
How machine learning conquers the unitary limitMachine learning has become a premier tool in physics and other fields of science. It has been shown that the quantum mechanical scattering problem can not only be solved with such techniques, but it…Bastian Kaspschak, Ulf-G. Meißner·Mar 20, 2020SaveLearn
Density-matrix based Extended Lagrangian Born-Oppenheimer Molecular DynamicsExtended Lagrangian Born-Oppenheimer molecular dynamics [ Phys.\ Rev.\ Lett.\ 2008, 100, 123004] is presented for Hartree-Fock theory, where the extended electronic degrees of…Anders M. N. Niklasson·Mar 19, 2020SaveLearn
An Efficient Proper Orthogonal Decomposition based Reduced-Order ModelThis paper presents a novel, more efficient proper orthogonal decomposition (POD) based reduced-order model (ROM) for compressible flows. In this POD model the governing equations, i.e., the…Elizabeth H. Krath, Forrest L. Carpenter, Paul G. A. Cizmas et al.·Mar 19, 2020SaveLearn
Computational Design of Stable and Highly Ion-conductive Materials using Multi-objective Bayesian Optimization: Case Studies on Diffusion of Oxygen and LithiumIon-conducting solid electrolytes are widely used for a variety of purposes. Therefore, designing highly ion-conductive materials is in strongly demand. Because of advancement in computers and…Masayuki Karasuyama, Hiroki Kasugai, Tomoyuki Tamura et al.·Mar 19, 2020SaveLearn
Revisiting the Common Neighbour Analysis and the Centrosymmetry ParameterWe review two standard methods for structural classification in simulations of crystalline phases, the Common Neighbour Analysis and the Centrosymmetry Parameter. We explore the definitions and…Peter M Larsen·Mar 19, 2020SaveLearn
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported CyberinfrastructureSignificant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and…E. A. Huerta, Asad Khan, Edward Davis et al.·Mar 18, 2020SaveLearn
Integrating State of the Art Compute, Communication, and Autotuning Strategies to Multiply the Performance of the Application Programm CPMD for Ab Initio Molecular Dynamics SimulationsWe present our recent code modernizations of the of the ab initio molecular dynamics program CPMD (www.cpmd.org) with a special focus on the ultra-soft pseudopotential (USPP) code path. Following the…Tobias Klöffel, Gerald Mathias, Bernd Meyer·Mar 18, 2020SaveLearn