Large scale simulation of pressure induced phase-field fracture propagation using UtopiaNon-linear phase field models are increasingly used for the simulation of fracture propagation models. The numerical simulation of fracture networks of realistic size requires the efficient parallel…Patrick Zulian, Alena Kopaničáková, Maria Giuseppina Chiara Nestola et al.·Jul 25, 2020SaveLearn
Fast evaluation of interaction integrals for confined systems with machine learningThe calculation of interaction integrals is a bottleneck for the treatment of many-body quantum systems due to its high numerical cost. We conduct configuration interaction calculations of the…Alina Mreńca-Kolasińska, Krzysztof Kolasiński, Bartłomiej Szafran·Jul 25, 2020SaveLearn
A Langevin dynamics approach for multi-layer mass transfer problemsWe use Langevin dynamics simulations to study the mass diffusion problem across two adjacent porous layers of different transport property. At the interface between the layers, we impose the…Oded Farago, Giuseppe Pontrelli·Jul 25, 2020SaveLearn
Restricted configuration path integral Monte CarloQuantum Monte Carlo belongs to the most accurate simulation techniques for quantum many-particle systems. However, for fermions, these simulations are hampered by the sign problem that prohibits…A. Yilmaz, K. Hunger, T. Dornheim et al.·Jul 24, 2020SaveLearn
An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulationsWe propose an unsupervised machine-learning checkpoint-restart (CR) algorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm features a particle compression stage…G. Chen, L. Chacon, T. B. Nguyen·Jul 23, 2020SaveLearn
Comparison of integral equations for the Maxwell transmission problem with general permittivitiesTwo recently derived integral equations for the Maxwell transmission problem are compared through numerical tests on simply connected axially symmetric domains for non-magnetic materials. The winning…Johan Helsing, Anders Karlsson, Andreas Rosén·Jul 23, 2020SaveLearn
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulationNon-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various…Romit Maulik, Themistoklis Botsas, Nesar Ramachandra et al.·Jul 23, 2020SaveLearn
A Vlasov Algorithm Derived from Phase Space ConservationExisting approaches to solving the Vlasov equation treat the system as a partial differential equation on a phase space grid, and track in either an Eulerian, Lagrangian, or semi-Lagrangian picture.…Jonathan P. Edelen, Stephen D. Webb·Jul 23, 2020SaveLearn
Power laws used to extrapolate the coupled cluster correlation energy to the thermodynamic limitRecent calculations using coupled cluster on solids have raised discussion of using a N-1/3 power law to fit the correlation energy when extrapolating to the thermodynamic limit, an approach…Tina N Mihm, Bingdi Yang, James J. Shepherd·Jul 22, 2020SaveLearn
Inverse Design of Plasmonic Structures with FDTDInverse design has greatly expanded nanophotonic devices and brought optimized performance. However, the use of inverse design for plasmonic structures has been challenging due to local field…Zhou Zeng, Xianfan Xu·Jul 22, 2020SaveLearn
Coarse Graining Molecular Dynamics with Graph Neural NetworksCoarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model must be formulated…Brooke E. Husic, Nicholas E. Charron, Dominik Lemm et al.·Jul 22, 2020SaveLearn
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structuresAutonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on…Teng Long, Nuno M. Fortunato, Ingo Opahle et al.·Jul 22, 2020SaveLearn
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex DomainsIn this paper, we propose multi-scale deep neural networks (MscaleDNNs) using the idea of radial scaling in frequency domain and activation functions with compact support. The radial scaling converts…Ziqi Liu, Wei Cai, Zhi-Qin John Xu·Jul 22, 2020SaveLearn
Hydrodynamics Across a Fluctuating InterfaceUnderstanding what happens inside the rippling and dancing surface of a liquid remains one of the great challenges of fluid dynamics. Using molecular dynamics (MD) we can pick apart the interface…Edward R. Smith, Carlos Braga·Jul 21, 2020SaveLearn
Efficient Formulation of Polarizable Gaussian Multipole Electrostatics for Biomolecular SimulationsMolecular dynamics simulations of biomolecules have been widely adopted in biomedical studies. As classical point-charge models continue to be used in routine biomolecular applications, there have…Haixin Wei, Ruxi Qi, Junmei Wang et al.·Jul 20, 2020SaveLearn
Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma ScienceWe have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of…Ge Dong, Kyle Gerard Felker, Alexey Svyatkovskiy et al.·Jul 20, 2020SaveLearn
GPU coprocessors as a service for deep learning inference in high energy physicsIn the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN…Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas et al.·Jul 20, 2020SaveLearn
Assessment of a symmetry preserving JFNK method for atmospheric convectionNumerical simulations of nonhydrostatic atmospheric flow, based on linearly decoupled semi-implicit or fully-implicit techniques, usually solve linear systems by a pre-conditioned Krylov method…M. Alamgir Hossain, Jahrul M Alam·Jul 20, 2020SaveLearn
Temperature-dependent properties of liquid-vapour coexistence system with many-body dissipative particle dynamics with energy conservationThe dynamic properties of fluid, including density, surface tension, diffusivity and viscosity, are temperature-dependent and can significantly influence the flow dynamics of mesoscopic…Kaixuan Zhang, Jie Li, Shuo Chen et al.·Jul 20, 2020SaveLearn
Machine Learning a Molecular Hamiltonian for Predicting Electron DynamicsWe develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting…Harish S. Bhat, Karnamohit Ranka, Christine M. Isborn·Jul 19, 2020SaveLearn
Limitations of Hartree-Fock with quantum resourcesThe Hartree-Fock problem provides the conceptual and mathematical underpinning of a large portion of quantum chemistry. As efforts in quantum technology aim to enhance computational chemistry…Sahil Gulania, James Daniel Whitfield·Jul 19, 2020SaveLearn
Cation interstitial diffusion in lead telluride and cadmium telluride studied by means of neural network potential based molecular dynamics simulationsUsing a recently developed approach to represent ab initio based force fields by a neural network potential, we perform molecular dynamics simulations of lead telluride (PbTe) and cadmium telluride…Marcin Mińkowski, Kerstin Hummer, Christoph Dellago·Jul 17, 2020SaveLearn
A general method for computing thermal magnetic noise arising from thin conducting objectsThermal motion of charge carriers in a conducting object causes magnetic field noise that interferes with sensitive measurements nearby the conductor. In this paper, we describe a method to compute…Joonas Iivanainen, Antti J. Mäkinen, Rasmus Zetter et al.·Jul 17, 2020SaveLearn
Spectral denoising for unsupervised analysis of correlated ionic transportComputation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement approach. We use…Nicola Molinari, Yu Xie, Ian Leifer et al.·Jul 17, 2020SaveLearn
PINNeik: Eikonal solution using physics-informed neural networksThe eikonal equation is utilized across a wide spectrum of science and engineering disciplines. In seismology, it regulates seismic wave traveltimes needed for applications like source localization,…Umair bin Waheed, Ehsan Haghighat, Tariq Alkhalifah et al.·Jul 16, 2020SaveLearn