Fast general two- and three-body interatomic potentialWe introduce a new class of machine learning interatomic potentials - fast General Two- and Three-body Potential (GTTP), which is as fast as conventional empirical potentials and require…Sergey Pozdnyakov, Artem R. Oganov, Efim Mazhnik et al.·Oct 16, 2019SaveLearn
Deep Coregionalization for the Emulation of Spatial-Temporal FieldsData-driven surrogate models are widely used for applications such as design optimization and uncertainty quantification, where repeated evaluations of an expensive simulator are required. For most…Wei Xing, Robert M. Kirby, Shandian Zhe·Oct 16, 2019SaveLearn
Orbital-dependent backflow wave functions for real-space quantum Monte CarloWe present and motivate an efficient way to include orbital dependent many--body correlations in trial wave function of real--space Quantum Monte Carlo methods for use in electronic structure…Markus Holzmann, Saverio Moroni·Oct 16, 2019SaveLearn
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulenceIn the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved…Suraj Pawar, Omer San, Adil Rasheed et al.·Oct 16, 2019SaveLearn
On preconditioning the self-consistent field iteration in real-space Density Functional TheoryWe present a real-space formulation for isotropic Fourier-space preconditioners used to accelerate the self-consistent field iteration in Density Functional Theory calculations. Specifically, after…Shashikant Kumar, Qimen Xu, Phanish Suryanarayana·Oct 15, 2019SaveLearn
Hybrid FFT algorithm for fast demagnetization field calculations on non-equidistant magnetic layersIn micromagnetic simulations, the demagnetization field is by far the computationally most expensive field component and often a limiting factor in large multilayer systems. We present an exact…Paul Heistracher, Florian Bruckner, Claas Abert et al.·Oct 15, 2019SaveLearn
Symplectic model reduction methods for the Vlasov equationParticle-based simulations of the Vlasov equation typically require a large number of particles, which leads to a high-dimensional system of ordinary differential equations. Solving such systems is…Tomasz M. Tyranowski, Michael Kraus·Oct 14, 2019SaveLearn
The Materials Simulation Toolkit for Machine Learning (MAST-ML): an automated open source toolkit to accelerate data-driven materials researchAs data science and machine learning methods are taking on an increasingly important role in the materials research community, there is a need for the development of machine learning software tools…Ryan Jacobs, Tam Mayeshiba, Ben Afflerbach et al.·Oct 14, 2019SaveLearn
Regularised Atomic Body-Ordered Permutation-Invariant Polynomials for the Construction of Interatomic PotentialsWe investigate the use of invariant polynomials in the construction of data-driven interatomic potentials for material systems. The "atomic body-ordered permutation-invariant polynomials"…Cas van der Oord, Geneviève Dusson, Gabor Csanyi et al.·Oct 14, 2019SaveLearn
Steady-state Simulation of Semiconductor Devices using Discontinuous Galerkin MethodsDesign of modern nanostructured semiconductor devices often calls for simulation tools capable of modeling arbitrarily-shaped multiscale geometries. In this work, to this end, a discontinuous…Liang Chen, Hakan Bagci·Oct 14, 2019SaveLearn
Variational Phase Field Formulations of Polarization and Phase Transition in Ferroelectric Thin FilmsElectric field plays an important role in ferroelectric phase transition. There have been numerous phase field formulations attempting to account for electrostatic interactions subject to different…Qiang Du, Ruotai Li, Lei Zhang·Oct 13, 2019SaveLearn
Machine Learning and Big Scientific DataThis paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton…Tony Hey, Keith Butler, Sam Jackson et al.·Oct 12, 2019SaveLearn
A Finite-Volume Method for Fluctuating Dynamical Density Functional TheoryWe introduce a finite-volume numerical scheme for solving stochastic gradient-flow equations. Such equations are of crucial importance within the framework of fluctuating hydrodynamics and dynamic…Antonio Russo, Sergio P. Perez, Miguel A. Durán-Olivencia et al.·Oct 11, 2019SaveLearn
Non-reciprocal wave transmission in a bilinear spring-mass systemSignificant amplitude-independent and passive non-reciprocal wave motion can be achieved in a one dimensional (1D) discrete chain of masses and springs with bilinear elastic stiffness. Some…Zhaocheng Lu, Andrew N. Norris·Oct 11, 2019SaveLearn
Predicting dynamical system evolution with residual neural networksForecasting time series and time-dependent data is a common problem in many applications. One typical example is solving ordinary differential equation (ODE) systems x=F(x). Oftentimes the…Artem Chashchin, Mikhail Botchev, Ivan Oseledets et al.·Oct 11, 2019SaveLearn
Correlated Gaussians and low-discrepancy sequencesWithin the Correlated Gaussian Method the parameters of the Gaussian basis functions are often chosen stochastically using pseudo-random sequences. We show that alternative low-discrepancy sequences,…D. V. Fedorov·Oct 11, 2019SaveLearn
A relativistic particle pusher for ultra-strong electromagnetic fieldsAbridged. Kinetic plasma simulations are nowadays commonly used to study a wealth of non-linear behaviours and properties in laboratory and space plasmas. In particular, in high-energy physics and…J. Pétri·Oct 10, 2019SaveLearn
Solving Optical Tomography with Deep LearningThis paper presents a neural network approach for solving two-dimensional optical tomography (OT) problems based on the radiative transfer equation. The mathematical problem of OT is to recover the…Yuwei Fan, Lexing Ying·Oct 10, 2019SaveLearn
Real-Time Reduced-Order Modeling of Stochastic Partial Differential Equations via Time-Dependent SubspacesWe present a new methodology for the real-time reduced-order modeling of stochastic partial differential equations called the dynamically/bi-orthonormal (DBO) decomposition. In this method, the…Prerna Patil, Hessam Babaee·Oct 9, 2019SaveLearn
Numerical solution of the Schrodinger equation for types of Woods-Saxon potentialIn this study, the Schrodinger equation for the Woods-Saxon potential, the general Woods-Saxon potential, and D-dimensional Woods-Saxon potential is numerically investigated.Vahid Mirzaei Mahmoud Abadi, Abbas Hosseini Ranjbar, Javad Mohammadi et al.·Oct 9, 2019SaveLearn
Phase-amplitude functional theory -- new ab initio calculation method for large size systemsNew method for ab initio calculations of the properties of large size system based on phase-amplitude functional is presented. It is shown that Schrodinger equation for many electrons complex system…Pawel Strak, Konrad Sakowski, Pawel Kempisty et al.·Oct 9, 2019SaveLearn
PiNN: A Python Library for Building Atomic Neural Networks of Molecules and MaterialsAtomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials. Despite many…Yunqi Shao, Matti Hellström, Pavlin D. Mitev et al.·Oct 8, 2019SaveLearn
A dynamically load-balanced parallel p -adaptive implicit high-order flux reconstruction method for under-resolved turbulence simulationWe present a dynamically load-balanced parallel p -adaptive implicit high-order flux reconstruction method for under-resolved turbulence simulation. The high-order explicit first stage, singly…Lai Wang, Matthias K. Gobbert, Meilin Yu·Oct 8, 2019SaveLearn
The Poisson-Boltzmann model for implicit solvation of electrolyte solutions: Quantum chemical implementation and assessment via Sechenov coefficientsWe present the theory and implementation of a Poisson-Boltzmann implicit solvation model for electrolyte solutions. This model can be combined with arbitrary electronic structure methods that provide…Christopher J. Stein, John M. Herbert, Martin Head-Gordon·Oct 8, 2019SaveLearn
Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network modelCrystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate. Using a neural network with convolutional layers…Ari Frankel, Kousuke Tachida, Reese Jones·Oct 8, 2019SaveLearn