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Making the most of data: Quantum Monte Carlo Post-Analysis RevisitedApr 22 2019In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and there are several ... More
The Structure of Graphene on Graphene/C60/Cu Interfaces: A Molecular Dynamics StudyApr 22 2019Two experimental studies reported the spontaneous formation of amorphous and crystalline structures of C60 intercalated between graphene and a substrate. They observed interesting phenomena ranging from reaction between C60 molecules under graphene to ... More
Acceleration of the Power Method with Dynamic Mode DecompositionApr 20 2019Presented is an algorithm based on dynamic mode decomposition (DMD) for acceleration of the power method (PM). The power method is a simple technique for determining the dominant eigenmode of an operator $\mathbf{A}$, and variants of the power method ... More
Global turbulence simulations of the tokamak edge region with GRILLIXApr 19 2019Turbulent dynamics in the scrape-off layer (SOL) of magnetic fusion devices is intermittent with large fluctuations in density and pressure. Therefore, a model is required that allows perturbations of similar or even larger magnitude to the time-averaged ... More
Semi-implicit methods for the dynamics of elastic sheetsApr 19 2019Recent applications (e.g. active gels and self-assembly of elastic sheets) motivate the need to efficiently simulate the dynamics of thin elastic sheets. We present semi-implicit time stepping algorithms to improve the time step constraints that arise ... More
Semi-Lagrangian Exponential Integration with application to the rotating shallow water equationsApr 19 2019In this paper we propose a novel way to integrate time-evolving partial differential equations that contain nonlinear advection and stiff linear operators, combining exponential integration techniques and semi-Lagrangian methods. The general formulation ... More
Spin wave dispersion of 3d ferromagnets based on QSGW calculationsApr 19 2019We have calculated spin wave (SW) dispersion by combining the quasi-particle self-consistent $GW$ (QSGW) and linear response method for transverse dynamical susceptibility $\chi^{+-}({\bf q},\omega)$. We compared the QSGW and the local density approximation ... More
Physical Symmetries Embedded in Neural NetworksApr 18 2019Neural networks are a central technique in machine learning. Recent years have seen a wave of interest in applying neural networks to physical systems for which the governing dynamics are known and expressed through differential equations. Two fundamental ... More
FPGA-accelerated machine learning inference as a service for particle physics computingApr 18 2019Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable ... More
DFT calculations of atoms and molecules in Cartesian gridsApr 18 2019Density functional theory (DFT) has emerged as one of the most versatile and lucrative approaches in electronic structure calculations of many-electron systems in past four decades. Here we give an account of the development of a variational DFT method ... More
Multigrid for Wilson Clover Fermions in GridApr 18 2019With the ever-growing number of computing architectures, performance portability is an important aspect of (Lattice QCD) software. The Grid library provides a good framework for writing such code, as it thoroughly separates hardware-specific code from ... More
Convergence of metadynamics: discussion of the adiabatic hypothesisApr 18 2019By drawing a parallel between metadynamics and self interacting models for polymers, we study the longtime convergence of the original metadynamics algorithm in the adiabatic setting, namely when the dynamics along the collective variables decouples from ... More
A spectral deferred correction strategy for low Mach number reacting flows subject to electric fieldsApr 17 2019We propose an algorithm for low Mach number reacting flows subjected to electric field that includes the chemical production and transport of charged species. This work is an extension of a multi-implicit spectral deferred correction (MISDC) algorithm ... More
Small-scale resolving simulations of the turbulent mixing in confined planar jets using one-dimensional turbulenceApr 17 2019Small-scale effects of turbulent mixing are numerically investigated by applying the map-based, stochastic, one-dimensional turbulence (ODT) model to confined planar jets. The model validation is carried out for the momentum transport by comparing ODT ... More
Effect of lattice shrinking on the migration of water within zeolite LTAApr 17 2019Water adsorption within zeolites of the Linde Type A (LTA) structure plays an important role in processes of water removal from solvents. For this purpose, knowing in which adsorption sites water is preferably found is of interest. In this paper, the ... More
Energy-weighted density matrix embedding of open correlated chemical fragmentsApr 16 2019We present a multi-scale approach to efficiently embed an ab initio correlated chemical fragment described by its energy-weighted density matrices, and entangled with a wider mean-field many-electron system. This approach, first presented in Phys. Rev. ... More
COMBIgor: data analysis package for combinatorial materials scienceApr 16 2019Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties. Due to maturation of combinatorial methods and their successful application in ... More
Reproducible Workflow on a Public Cloud for Computational Fluid DynamicsApr 16 2019In a new effort to make our research transparent and reproducible by others, we developed a workflow to run computational studies on a public cloud. It uses Docker containers to create an image of the application software stack. We also adopt several ... More
Numerical study of tearing mode seeding in tokamak X-point plasmaApr 16 2019A detailed understanding of island seeding is crucial to avoid (N)TMs and their negative consequences like confinement degradation and disruptions. In the present work, we investigate the growth of 2/1 islands in response to magnetic perturbations. Although ... More
Self-gravitational Force Calculation of High-Order Accuracy for Infinitesimally Thin Gaseous DisksApr 16 2019Self-gravitational force calculation for infinitesimally thin disks is important for studies on the evolution of galactic and protoplanetary disks. Although high-order methods have been developed for hydrodynamic and magneto-hydrodynamic equations, high-order ... More
Dimensionless solutions and general characteristics of bioheat transfer during thermal therapyApr 15 2019The derivation and application of the general characteristics of bioheat transfer for medical applications are shown in this paper. Two general bioheat transfer characteristics are derived from solutions of one-dimensional Pennes' bioheat transfer equation; ... More
Naphthalene crystal shape prediction from molecular dynamics simulationsApr 15 2019We used molecular dynamics simulations to predict the steady state crystal shape of naphthalene grown from ethanol solution. The simulations were performed at constant supersaturation by utilizing a recently proposed algorithm [Perego et al., J. Chem. ... More
Solving Differential Equation with Constrained Multilayer Feedforward NetworkApr 14 2019In this paper, we present a novel framework to solve differential equations based on multilayer feedforward network. Previous works indicate that solvers based on neural network have low accuracy due to that the boundary conditions are not satisfied accurately. ... More
Deep-learning PDEs with unlabeled data and hardwiring physics lawsApr 13 2019Providing fast and accurate solutions to partial differential equations is a problem of continuous interest to the fields of applied mathematics and physics. With the recent advances in machine learning, the adoption learning techniques in this domain ... More
Quantum Point Contact Parameter Extraction of Carbon-based Resistive Memory using Hybrid Genetic AlgorithmApr 13 2019Resistive switching phenomenon in carbon film is associated with formation and annihilation of low resistance sp2 nanochannels within the amorphous sp3 matrix. The thinnest point of these graphitic nanochannels behaves like quantum wire (QW) and limits ... More
An a posteriori verification method for generalized Hermitian eigenvalue problems in large-scale electronic state calculationsApr 13 2019An a posteriori verification method is proposed for the generalized Hermitian eigenvalue problems that appear in large-scale electronic state calculations. The method is realized by the two stage process in which the approximate solution is generated ... More
Nanoparticle diffusion in sheared cellular blood flowApr 13 2019Using a multiscale blood flow solver, the complete diffusion tensor of nanoparticle (NP) in sheared cellular blood flow is calculated over a wide range of shear rate and haematocrit. In the short-time regime, NPs exhibit anomalous dispersive behaviors ... More
On the validity of the Arrhenius picture in two-dimensional submonolayer growthApr 12 2019For surface-mediated processes, such as on-surface synthesis, epitaxial growth and heterogeneous catalysis, a constant slope in the Arrhenius diagram of the corresponding rate of interest against inverse temperature, $\log R$ {\it vs} $1/k_B T$, is traditionally ... More
Approximation of tensor fields on surfaces of arbitrary topology based on local Monge parametrizationsApr 12 2019We introduce a new method, the Local Monge Parametrizations (LMP) method, to approximate tensor fields on general surfaces given by a collection of local parametrizations, e.g.~as in finite element or NURBS surface representations. Our goal is to use ... More
Carbon nanotube array as a two-dimensional hyperbolic material: ab-initio studyApr 12 2019We use an ab-initio approach to design and study a novel two-dimensional material - a planar array of carbon nanotubes separated by an optimal distance defined by the van der Waals interaction. We show that the energy spectrum for an array of quasi-metallic ... More
A group theoretical approach to computing phonons and their interactionsApr 12 2019Here we present four independent advances which facilitate the computation of phonons and their interactions from first-principles. First, we implement a group-theoretical approach to construct the order N Taylor series of a d-dimensional crystal purely ... More
Acoustic cloaking: geometric transform, homogenization and a genetic algorithmApr 12 2019A general process is proposed to experimentally design anisotropic inhomogeneous metamaterials obtained through a change of coordinate in the Helmholtz equation. The method is applied to the case of a cylindrical transformation that allows to perform ... More
Comparing particle-particle and particle-hole channels of random-phase approximationApr 12 2019We present a comparative study of particle-hole and particle-particle channels of random-phase approximation (RPA) for molecular dissociations of different bonding types. We introduced a \textit{direct} particle-particle RPA scheme, in analogy to the ... More
Deep learning methods based on cross-section images for predicting effective thermal conductivity of compositesApr 12 2019Effective thermal conductivity is an important property of composites for different thermal management applications. Although physics-based methods, such as effective medium theory and solving partial differential equation, dominate the relevant research, ... More
EPSR++: An Open Source Empirical Potential Structure Refinement Neutron Data Analysis Framework Supporting Parallel Across Computer Cluster Nodes and GPU Hardware AccelerationApr 12 2019Empirical Potential Structure Refinement (EPSR) is a neutron scattering data analysis algorithm and software package. It was developed in the British Spallation Neutron Source (ISIS) Disordered Materials Group, aims at constructing most-probable all-atom ... More
Inferring crystal electronic properties from experimental data sets through Semidefinite ProgrammingApr 11 2019Constructing a quantum description of crystals from scattering experiments is of paramount importance to explain their macroscopic properties and to evaluate the pertinence of theoretical ab-initio models. While reconstruction methods of the one-electron ... More
Inferring the quantum density matrix with machine learningApr 11 2019We introduce two methods for estimating the density matrix for a quantum system: Quantum Maximum Likelihood and Quantum Variational Inference. In these methods, we construct a variational family to model the density matrix of a mixed quantum state. We ... More
Evolution of star clusters on eccentric orbits: semi-analytical approachApr 11 2019We study the dynamical evolution of star clusters on eccentric orbits using a semi-analytical approach. In particular we adapt and extend the equations of EMACSS code, introduced by Gieles et al. (2014), to work with eccentric orbits. We follow the evolution ... More
Compressing deep neural networks by matrix product operatorsApr 11 2019A deep neural network is a parameterization of a multi-layer mapping of signals in terms of many alternatively arranged linear and nonlinear transformations. The linear transformations, which are generally used in the fully-connected as well as convolutional ... More
Big-Data-Driven Materials Science and its FAIR Data InfrastructureApr 11 2019This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing ... More
Machine learning optimization of the collocation point set for solving the electronic Schrödinger equationApr 11 2019The rectangular collocation approach makes it possible to solve the Schr\"odinger equation with basis functions that do not have amplitude in all regions in which wavefunctions have significant amplitude. Collocation points can be restricted to a small ... More
Curvilinear coordinate Generalized Source Method for gratings having continuous piecewise-smooth profilesApr 11 2019High-efficient direct numerical methods are currently in demand for optimization procedures in the fields of both conventional diffractive and metasurface optics. With a view of extending the scope of application of the previously proposed Generalized ... More
The Stefan problem with variable thermophysical properties and phase change temperatureApr 11 2019In this paper we formulate a Stefan problem appropriate when the thermophysical properties are distinct in each phase and the phase-change temperature is size or velocity dependent. Thermophysical properties invariably take different values in different ... More
Joint Reconstruction in Low Dose Multi-Energy CTApr 11 2019Multi-energy CT takes advantage of the non-linearly varying attenuation properties of elemental media with respect to energy, enabling more precise material identification than single-energy CT. The increased precision comes with the cost of a higher ... More
Stochastic vertex corrections: $GWΓ_X$ approach for accurate quasiparticle energiesApr 11 2019Quasiparticle energies for the highest occupied and lowest unoccupied states are investigated using the stochastic many-body perturbation theory with the inclusion of non-local vertex corrections. Approximate form of a vertex function, labeled $G_0W_0^{tc}\Gamma_X$, ... More
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge DetectionApr 10 2019Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification ... More
Spatial coupling of an explicit temporal adaptive integration scheme with an implicit time integration schemeApr 10 2019The Reynolds-Averaged Navier-Stokes equations and the Large-Eddy Simulation equations can be coupled using a transition function to switch from a set of equations applied in some areas of a domain to the other set in the other part of the domain. Following ... More
Simulation of hyperelastic materials in real-time using Deep LearningApr 10 2019The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, ... More
Compressed sensing reconstruction using Expectation PropagationApr 10 2019Many interesting problems in fields ranging from telecommunications to computational biology can be formalized in terms of large underdetermined systems of linear equations with additional constraints or regularizers. One of the most studied ones, the ... More
Dependence of the transportation time on the sequence in which particles with different hopping probabilities enter a latticeApr 10 2019Smooth transportation has drawn the attention of many researchers and practitioners in several fields. In the present paper, we propose a modified model of a totally asymmetric simple exclusion process (TASEP), which includes multiple species of particles ... More
Intrinsic Ferromagnetism in ElectrenesApr 10 2019We report intrinsic ferromagnetism in monolayer electrides or electrenes, in which excess electrons act as anions. Our first-principles calculations demonstrate that magnetism in such electron-rich two-dimensional (2D) materials originates from the anionic ... More
A multiscale model for Rayleigh-Taylor and Richtmyer-Meshkov instabilitiesApr 09 2019We develop a novel multiscale model of interface motion for the Rayleigh-Taylor instability (RTI) and Richtmyer-Meshkov instability (RMI) for two-dimensional, inviscid, compressible flows with vorticity, which yields a fast-running numerical algorithm ... More
Isotope Effects in Liquid Water via Deep Potential Molecular DynamicsApr 09 2019A comprehensive microscopic understanding of ambient liquid water is a major challenge for $ab$ $initio$ simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive ... More
Flying in a superfluid: starting flow past an airfoilApr 09 2019We investigate the development of superfluid flow around an airfoil accelerated to a finite velocity from rest. Using both simulations of the Gross-Pitaevskii equation and analytical calculations we find striking similarities to viscous flows: from the ... More
Deep reinforcement learning for robust quantum optimizationApr 09 2019Machine learning techniques based on artificial neural networks have been successfully applied to solve many problems in science. One of the most interesting domains of machine learning, reinforcement learning, has natural applicability for optimization ... More
MolMod: An open access database of force fields for molecular simulations of fluidsApr 09 2019The MolMod database is presented, which is openly accessible at http://molmod.boltzmann-zuse.de/ and contains presently intermolecular force fields for over 150 pure fluids. It was developed and is maintained by the Boltzmann-Zuse Society for Computational ... More
Taming chaos to sample rare events: the effect of weak chaosApr 09 2019Rare events in non-linear dynamical systems are difficult to sample because of the sensitivity to perturbations of initial conditions and of complex landscapes in phase space. Here we discuss strategies to control these difficulties and succeed in obtainining ... More
A comparative study of implicit Jacobian-free Rosenbrock-Wanner, ESDIRK and BDF methods for unsteady flow simulation with high-order flux reconstruction formulationsApr 09 2019We conduct a comparative study of the Jacobian-free linearly implicit Rosenbrock-Wanner (ROW) methods, the explicit first stage, singly diagonally implicit Runge-Kutta (ESDIRK) methods, and the second-order backward differentiation formula (BDF2) for ... More
Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamicsApr 08 2019We establish a time-stepping learning algorithm and apply it to predict the solution of the partial differential equation of motion in micromagnetism as a dynamical system depending on the external field as parameter. The data-driven approach is based ... More
Parallel three-dimensional simulations of quasi-static elastoplastic solids. Part II: Coordinate transformationsApr 08 2019In this two-part paper, we extend to three dimensions a new projection method for simulating hypo-elastoplastic solids in the quasi-static limit. The method is based on a surprising mathematical correspondence to the incompressible Navier-Stokes equations, ... More
Strain Effects on the Mechanical Properties of Group-V Monolayers with Buckled Honeycomb StructuresApr 08 2019Based on first-principles calculations, we study systematically the ideal tensile stress-strain relations of three monoatomic group-V monolayer two dimensional (2D) materials with buckled honeycomb lattices: blue phosphorene, arsenene, and antimonene. ... More
maze: Heterogeneous Ligand Unbinding along Transient Protein TunnelsApr 08 2019Recent developments in enhanced sampling methods showed that it is possible to reconstruct ligand unbinding pathways with spatial and temporal resolution inaccessible to experiments. Ideally, such techniques should provide an atomistic definition of possibly ... More
Lattice QCD on upcoming Arm architecturesApr 08 2019Recently Arm introduced a new instruction set called Scalable Vector Extension (SVE), which supports vector lengths up to 2048 bits. While SVE hardware will not be generally available until about 2021, we believe that future SVE-based architectures will ... More
Parallel three-dimensional simulations of quasi-static elastoplastic solids. Part I: Numerical formulation and examplesApr 08 2019In this two-part paper, we extend to three dimensions a new projection method for simulating hypo-elastoplastic solids in the quasi-static limit. The method is based on a surprising mathematical correspondence to the incompressible Navier-Stokes equations, ... More
Linking plastic heterogeneity of bulk metallic glasses to quench-in structural defects with machine learningApr 07 2019When metallic glasses are subjected to mechanical loads, the plastic response of atoms is heterogeneous. However, the degree to which the plastic units are correlated with the structural defects frozen in the quenched glass structure is still elusive. ... More
Bayesian machine learning for quantum molecular dynamicsApr 07 2019This article discusses applications of Bayesian machine learning for quantum molecular dynamics. One particular formulation of quantum dynamics advocated here is in the form of a machine learning simulator of the Schr\"{o}dinger equation. If combined ... More
Dissipative Particle Dynamics for Systems with Polar SpeciesApr 07 2019In this work we developed a method for simulating polar species in the dissipative particle dynamics (DPD) method. The main idea behind the method is to treat each bead as a dumb-bell, i.e. two sub-beads (the sub-beads can bear charges) kept at a fixed ... More
Dissipative Particle Dynamics for Systems with Polar SpeciesApr 07 2019Apr 16 2019In this work we developed a method for simulating polar species in the dissipative particle dynamics (DPD) method. The main idea behind the method is to treat each bead as a dumb-bell, i.e. two sub-beads (the sub-beads can bear charges) kept at a fixed ... More
A unified Eulerian framework for multimaterial continuum mechanicsApr 07 2019A framework for simulating the interactions between multiple different continua is presented. Each constituent material is governed by the same set of equations, differing only in terms of their equations of state and strain dissipation functions. The ... More
Parametrization of stochastic inputs using generative adversarial networks with application in geologyApr 07 2019We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric ... More
Parametrization of stochastic inputs using generative adversarial networks with application in geologyApr 07 2019Apr 09 2019We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric ... More
Brute-forcing spin-glass problems with CUDAApr 07 2019We demonstrate how to compute the low energy spectrum for small ($L\le 50$), but otherwise arbitrary, spin-glass instances using modern Graphics Processing Units or a similar heterogeneous architecture. Our algorithm performs an exhaustive (i.e. brute-force) ... More
Improved three-dimensional color-gradient lattice Boltzmann model for immiscible multiphase flowsApr 07 2019In this paper, an improved three-dimensional color-gradient lattice Boltzmann (LB) model is proposed for simulating immiscible multiphase flows. Compared with the previous three-dimensional color-gradient LB models, which suffer from the lack of Galilean ... More
TURTLE: A C library for an optimistic stepping through a topographyApr 06 2019TURTLE is a C library providing utilities allowing to navigate through a topography described by a Digital Elevation Model (DEM). The library has been primarily designed for the Monte-Carlo transport of particles scattering over medium to long ranges, ... More
Computer Vision Approach to Study Deformation of MaterialsApr 05 2019Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this study, the possibility of utilizing a computer vision algorithm to extract ... More
Discrete Fourier Transform Improves the Prediction of the Electronic Properties of Molecules in Quantum Machine LearningApr 05 2019High-throughput approximations of quantum mechanics calculations and combinatorial experiments have been traditionally used to reduce the search space of possible molecules, drugs and materials. However, the interplay of structural and chemical degrees ... More
SpM: Sparse modeling tool for analytic continuation of imaginary-time Green's functionApr 05 2019We present SpM, a sparse modeling tool for the analytic continuation of imaginary-time Green's function, licensed under GNU General Public License version 3. In quantum Monte Carlo simulation, dynamic physical quantities such as single-particle and magnetic ... More
Rear-surface integral method for calculating thermal diffusivity: finite pulse time correction and two-layer samplesApr 05 2019We study methods for calculating the thermal diffusivity of solids from laser flash experiments. This experiment involves subjecting the front surface of a small sample of the material to a heat pulse and recording the resulting temperature rise on the ... More
A Novel Ten-Moment Multifluid Model for Mercury: From the Planetary Conducting Core to the Dynamic MagnetosphereApr 04 2019We have developed a three-dimensional ten-moment multifluid model and applied it to investigate the tightly coupled interior-magnetosphere system of Mercury. This novel kinetic fluid model incorporates the non-ideal effects including the Hall effect, ... More
Fast and Robust Algorithm for the Minimisation of the Energy of Spin SystemsApr 04 2019An optimization algorithm based on orthogonal matrix transformations is presented for the minimisation of the energy of a magnetic system with respect to spin orientations. When combined with the limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) ... More
Sampling Limits for Electron Tomography with Sparsity-exploiting ReconstructionsApr 04 2019Electron tomography (ET) has become a standard technique for 3D characterization of materials at the nano-scale. Traditional reconstruction algorithms such as weighted back projection suffer from disruptive artifacts with insufficient projections. Popularized ... More
A Practical Guide to Surface Kinetic Monte Carlo SimulationsApr 04 2019This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out ... More
Comparison between isothermal collision-streaming and finite-difference lattice Boltzmann modelsApr 04 2019We present here a comparison between collision-streaming and finite-difference lattice Boltzmann (LB) models. This study provides a derivation of useful formulae which help one to properly compare the simulation results obtained with both LB models. We ... More
A scalable Controlled NOT gate for linear optical computing using microring resonatorsApr 03 2019We propose a scalable version of a KLM CNOT gate based upon integrated waveguide microring resonators (MRR), vs the original KLM-approach using beam splitters (BS). The core element of our CNOT gate is a nonlinear phase-shift gate (NLPSG) using three ... More
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare EventsApr 03 2019Training machine learning based interatomic potentials often requires thousands of first principles calculations, severely limiting their practical application. We present an adaptive Bayesian inference method for automating and accelerating the on-the-fly ... More
Large Second-Harmonic Generation and Linear Electro-Optic Effect in Trigonal Selenium and TelluriumApr 03 2019Trigonal selenium and tellurium crystalize in helical chain-like structures and thus possess interesting properties such as current-induced spin polarization, gyrotropic effects and nonlinear optical responses. By performing systematic ab initio calculations ... More
Unified Gas-kinetic Wave-Particle Methods III: Multiscale Photon TransportApr 03 2019In this paper, we extend the unified gas-kinetic wave-particle (UGKWP) method to the multiscale photon transport. In this method, the photon free streaming and scattering processes are treated in an un-splitting way. The duality descriptions, namely the ... More
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relationsApr 02 2019We investigate the use of discrete and continuous versions of physics-informed neural network methods for learning unknown dynamics or constitutive relations of a dynamical system. For the case of unknown dynamics, we represent all the dynamics with a ... More
Atomic-scale representation and statistical learning of tensorial propertiesApr 02 2019This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, ... More
A Constrained Transport Method for the Solution of the Resistive Relativistic MHD EquationsApr 02 2019We describe a novel Godunov-type numerical method for solving the equations of resistive relativistic magnetohydrodynamics. In the proposed approach, the spatial components of both magnetic and electric fields are located at zone interfaces and are evolved ... More
Neural networks based variationally enhanced samplingApr 02 2019Apr 03 2019Sampling complex free energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number ... More
Neural networks based variationally enhanced samplingApr 02 2019Sampling complex free energy surfaces is one of the challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key ... More
Ferroelectricity with Asymmetric Hysteresis in Metallic LiOsO3 Ultrathin FilmsApr 02 2019Bulk LiOsO3 was experimentally identified as a "ferroelectric" metal where polar distortions coexist with metallicity [Shi et al., Nature Materials 12, 1024 (2013)]. It is generally believed that polar displacements in a "ferroelectric" metal cannot be ... More
A general-purpose element-based approach to compute dispersion relations in periodic materials with existing finite element codesApr 02 2019In most of standard Finite Element (FE) codes it is not easy to calculate dispersion relations from periodic materials. Here we propose a new strategy to calculate such dispersion relations with available FE codes using user element subroutines. Typically, ... More
Q# and NWChem: Tools for Scalable Quantum Chemistry on Quantum ComputersApr 01 2019Fault-tolerant quantum computation promises to solve outstanding problems in quantum chemistry within the next decade. Realizing this promise requires scalable tools that allow users to translate descriptions of electronic structure problems to optimized ... More
Fast, accurate, and transferable many-body interatomic potentials by genetic programmingApr 01 2019The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate ... More
Fast, accurate, and transferable many-body interatomic potentials by genetic programmingApr 01 2019Apr 07 2019The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop fast and accurate ... More
Classical Nucleation Theory for the Crystallization Kinetics in Sheared LiquidsApr 01 2019While statistical mechanics provides a comprehensive framework for the understanding of equilibrium phase behavior, predicting the kinetics of phase transformations remains a challenge. Classical nucleation theory (CNT) provides a thermodynamic framework ... More
An Atomistic Machine Learning Package for Surface Science and CatalysisApr 01 2019We present work flows and a software module for machine learning model building in surface science and heterogeneous catalysis. This includes fingerprinting atomic structures from 3D structure and/or connectivity information, it includes descriptor selection ... More
Rotating magnetic fields driven antiferromagnetic domain wall motionsApr 01 2019In this work, we study the rotating magnetic fields driven domain wall motions in antiferromagnetic nanowires, using the numerical simulations of the classical Heisenberg spin model. We show that in low frequency region, the rotating field alone could ... More