Parallel Transport Time-Dependent Density Functional Theory Calculations with Hybrid Functional on SummitReal-time time-dependent density functional theory (rt-TDDFT) with hybrid exchange-correlation functional has wide-ranging applications in chemistry and material science simulations. However, it can…Weile Jia, Lin-Wang Wang, Lin Lin·May 3, 2019SaveLearn
GPU accelerated fast multipole boundary element method for simulation of 3D bubble dynamics in potential flowA numerical method for simulation of bubble dynamics in three-dimensional potential flows is presented. The approach is based on the boundary element method for the Laplace equation accelerated via…Nail A. Gumerov, Yulia A. Pityuk, Olga A. Abramova et al.·May 3, 2019SaveLearn
TensorNetwork: A Library for Physics and Machine LearningTensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are…Chase Roberts, Ashley Milsted, Martin Ganahl et al.·May 3, 2019SaveLearn
Modeling tissue perfusion in terms of 1d-3d embedded mixed-dimension coupled problems with distributed sourcesWe present a new method for modeling tissue perfusion on the capillary scale. The microvasculature is represented by a network of one-dimensional vessel segments embedded in the extra-vascular space.…Timo Koch, Martin Schneider, Rainer Helmig et al.·May 2, 2019SaveLearn
Modeling elastic properties of polystyrene through coarse-grained molecular dynamics simulationsThis paper presents an extended coarse-grained investigation of the elastic properties of polystyrene. In particular, we employ the well-known MARTINI force field and its modifications to perform…Yaroslav Beltukov, Igor Gula, Alexander M. Samsonov et al.·May 2, 2019SaveLearn
Hamiltonian symmetries in auxiliary-field quantum Monte Carlo calculations for electronic structureWe describe how to incorporate symmetries of the Hamiltonian into auxiliary-field quantum Monte Carlo calculations (AFQMC). Focusing on the case of Abelian symmetries, we show that the computational…Mario Motta, Shiwei Zhang, Garnet Kin-Lic Chan·May 1, 2019SaveLearn
Analytical PAW Projector Functions for Reduced Bandwidth RequirementsLarge scale electronic structure calculations require modern high performance computing (HPC) resources and, as important, mature HPC applications that can make efficient use of those. Real-space…Paul F. Baumeister, Shigeru Tsukamoto·May 1, 2019SaveLearn
DFTB modelling of lithium intercalated graphite with machine-learned repulsive potentialLithium ion batteries have been a central part of consumer electronics for decades. More recently, they have also become critical components in the quickly arising technological fields of electric…Chiara Panosetti, Simon B. Anniés, Cristina Grosu et al.·Apr 30, 2019SaveLearn
An asymptotic preserving well-balanced scheme for the isothermal fluid equations in low-temperature plasma applicationsWe present a novel numerical scheme for the efficient and accurate solution of the isothermal two-fluid (electron and ion) equations coupled to Poisson's equation for low-temperature plasmas. The…Alejandro Alvarez Laguna, Teddy Pichard, Thierry Magin et al.·Apr 30, 2019SaveLearn
Comprehensive comparison of collision models in the lattice Boltzmann framework: Theoretical investigationsOver the last decades, several types of collision models have been proposed to extend the validity domain of the lattice Boltzmann method (LBM), each of them being introduced in its own formalism.…C. Coreixas, B. Chopard, J. Latt·Apr 29, 2019SaveLearn
The Scikit-HEP ProjectThe Scikit-HEP project is a community-driven and community-oriented effort with the aim of providing Particle Physics at large with a Python scientific toolset containing core and common tools. The…Eduardo Rodrigues·Apr 29, 2019SaveLearn
Wave Physics as an Analog Recurrent Neural NetworkAnalog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for…Tyler W. Hughes, Ian A. D. Williamson, Momchil Minkov et al.·Apr 29, 2019SaveLearn
A new software implementation of the Oslo method with rigorous statistical uncertainty propagationThe Oslo method comprises a set of analysis techniques designed to extract nuclear level density and average γ-decay strength function from a set of excitation-energy tagged γ-ray spectra. Here…Jørgen E. Midtbø, Fabio Zeiser, Erlend Lima et al.·Apr 29, 2019SaveLearn
Electronic Properties of Defective MoS2 Monolayers Subject to Mechanical Deformations: A First-Principles ApproachMonolayers (ML) of Group-6 transition-metal dichalcogenides (TMDs) are semiconducting two-dimensional materials with direct bandgap, showing promising applications in various fields of science and…Mohammad Bahmani, Mahdi Faghihnasiri, Michael Lorke et al.·Apr 29, 2019SaveLearn
High-order DG solvers for under-resolved turbulent incompressible flows: A comparison of L2 and H(div) methodsThe accurate numerical simulation of turbulent incompressible flows is a challenging topic in computational fluid dynamics. For discretisation methods to be robust in the under-resolved regime, mass…Niklas Fehn, Martin Kronbichler, Christoph Lehrenfeld et al.·Apr 29, 2019SaveLearn
SPH-EXA: Enhancing the Scalability of SPH codes Via an Exascale-Ready SPH Mini-AppNumerical simulations of fluids in astrophysics and computational fluid dynamics (CFD) are among the most computationally-demanding calculations, in terms of sustained floating-point operations per…Danilo Guerrera, Aurélien Cavelan, Rubén M. Cabezón et al.·Apr 29, 2019SaveLearn
Constraint-Aware Neural Networks for Riemann ProblemsNeural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such…Jim Magiera, Deep Ray, Jan S. Hesthaven et al.·Apr 29, 2019SaveLearn
Phase-field-based lattice Boltzmann model for immiscible incompressible N-phase flowsIn this paper, we develop an efficient lattice Boltzmann (LB) model for simulating immiscible incompressible N-phase flows (N ≥ 2) based on the Cahn-Hilliard phase field theory. In order to…Xiaolei Yuan, Hong Liang, Zhenhua Chai et al.·Apr 29, 2019SaveLearn
Ligament break-up simulation through pseudo-potential Lattice Boltzmann MethodThe Plateau-Rayleigh instability causes the fragmentation of a liquid ligament into smaller droplets. In this study a numerical study of this phenomenon based on a single relaxation time (SRT)…Daniele Chiappini, Xiao Xue, Giacomo Falcucci et al.·Apr 26, 2019SaveLearn
Mesoscale simulation of soft particles with tunable contact angle in multi-component fluidsSoft particles at fluid interfaces play an important role in many aspects of our daily life, such as the food industry, paints and coatings, and medical applications. Analytical methods are not…Maarten Wouters, Othmane Aouane, Timm Krueger et al.·Apr 25, 2019SaveLearn
Electrostatic T-matrix for a torus on bases of toroidal and spherical harmonicsSemi-analytic expressions for the static limit of the T-matrix for electromagnetic scattering are derived for a circular torus, expressed in bases of both toroidal and spherical harmonics. The…Matt Majic·Apr 24, 2019SaveLearn
An efficient numerical framework for the amplitude expansion of the phase-field crystal modelThe study of polycrystalline materials requires theoretical and computational techniques enabling multiscale investigations. The amplitude expansion of the phase field crystal model (APFC) allows for…Simon Praetorius, Marco Salvalaglio, Axel Voigt·Apr 24, 2019SaveLearn
Artificial Neural Networks as Trial Wave Functions for Quantum Monte CarloInspired by the universal approximation theorem and widespread adoption of artificial neural network techniques in a diversity of fields, we propose feed-forward neural networks as a general purpose…Jan Kessler, Francesco Calcavecchia, Thomas D. Kühne·Apr 23, 2019SaveLearn
Entropy: The former trouble with particles (including a new numerical model computational penalty for the Akaike information criterion)Traditional random-walk particle-tracking (PT) models of advection and dispersion do not track entropy, because particle masses remain constant. Newer mass-transfer particle tracking (MTPT) models…David A. Benson, Stephen Pankavich, Michael Schmidt et al.·Apr 23, 2019SaveLearn
Multicomponent Flow on Curved Surfaces: A Vielbein Lattice Boltzmann ApproachWe develop and implement a novel lattice Boltzmann scheme to study multicomponent flows on curved surfaces, coupling the continuity and Navier-Stokes equations with the Cahn-Hilliard equation to…Victor E. Ambruş, Sergiu Busuioc, Alexander J. Wagner et al.·Apr 22, 2019SaveLearn