A numerical-continuation-enhanced flexible boundary condition scheme applied to Mode I and Mode III fractureMotivated by the inadequacy of conducting atomistic simulations of crack propagation using static boundary conditions that do not reflect the movement of the crack tip, we extend Sinclair's flexible…Maciej Buze, James R. Kermode·Aug 28, 2020SaveLearn
Reconstructing the Scattering Matrix from Scanning Electron Diffraction Measurements AloneThree-dimensional phase contrast imaging of multiply-scattering samples in X-ray and electron microscopy is extremely challenging, due to small numerical apertures, the unavailability of wavefront…Philipp M Pelz, Hamish G Brown, Jim Ciston et al.·Aug 28, 2020SaveLearn
A transfer learning metamodel using artificial neural networks applied to natural convection flows in enclosuresIn this paper, we employed a transfer learning technique to predict the Nusselt number for natural convection flows in enclosures. Specifically, we considered the benchmark problem of a…Majid Ashouri, Alireza Hashemi·Aug 28, 2020SaveLearn
Uncoupling electrokinetic flow solutionsThe continuum-scale electrokinetic porous-media flow and excess charge redistribution equations are uncoupled using eigenvalue decomposition. The uncoupling results in a pair of independent diffusion…Kristopher L. Kuhlman, Bwalya Malama·Aug 28, 2020SaveLearn
Multi-scale approach for the prediction of atomic scale propertiesElectronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemical bonds. Locality…Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti·Aug 27, 2020SaveLearn
A provably entropy stable subcell shock capturing approach for high order split form DG for the compressible Euler EquationsThe main result in this paper is a provably entropy stable shock capturing approach for the high order entropy stable DGSEM based on a hybrid blending with a subcell low order variant. Since it is…Sebastian Hennemann, Andrés M. Rueda-Ramírez, Florian J. Hindenlang et al.·Aug 27, 2020SaveLearn
Molecular Simulation of Electrode-Solution InterfacesMany key industrial processes, from electricity production, conversion and storage to electrocatalysis or electrochemistry in general, rely on physical mechanisms occurring at the interface between a…Laura Scalfi, Mathieu Salanne, Benjamin Rotenberg·Aug 27, 2020SaveLearn
Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode DecompositionRadiation-induced photocurrent in semiconductor devices can be simulated using complex physics-based models, which are accurate, but computationally expensive. This presents a challenge for…Joshua Hanson, Pavel Bochev, Biliana Paskaleva·Aug 27, 2020SaveLearn
Learning Unknown Physics of non-Newtonian FluidsWe extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The…Brandon Reyes, Amanda A. Howard, Paris Perdikaris et al.·Aug 26, 2020SaveLearn
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of StaneneWe present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional…Yu Xie, Jonathan Vandermause, Lixin Sun et al.·Aug 26, 2020SaveLearn
Multiple Scattering Theory for Dense PlasmasDense plasmas occur in stars, giant planets and in inertial fusion experiments. Accurate modeling of the electronic structure of these plasmas allows for prediction of material properties that can in…Charles E. Starrett, Nathaniel Shaffer·Aug 26, 2020SaveLearn
A neural network multigrid solver for the Navier-Stokes equationsWe present the deep neural network multigrid solver (DNN-MG) that we develop for the instationary Navier-Stokes equations. DNN-MG improves computational efficiency using a judicious combination of a…Nils Margenberg, Dirk Hartmann, Christian Lessig et al.·Aug 26, 2020SaveLearn
Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimerThe goal of the present work is to obtain accurate potential energy surfaces (PES) for high-dimensional molecular systems with a small number of ab initio calculations in a…Hiroki Sugisawa, Tomonori Ida, Roman V. Krems·Aug 26, 2020SaveLearn
Enabling robust offline active learning for machine learning potentials using simple physics-based priorsMachine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a…Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen et al.·Aug 25, 2020SaveLearn
A Bin and Hash Method for Analyzing Reference Data and Descriptors in Machine Learning PotentialsIn recent years the development of machine learning (ML) potentials (MLP) has become a very active field of research. Numerous approaches have been proposed, which allow to perform extended…Martín Leandro Paleico, Jörg Behler·Aug 25, 2020SaveLearn
Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part IIn this paper the physics- (or PDE-) integrated machine learning (ML) framework is investigated. The Navier-Stokes (NS) equations are solved using Tensorflow library for Python via Chorin's…Arsen S. Iskhakov, Nam T. Dinh·Aug 24, 2020SaveLearn
Requirements for very high temperature Kohn-Sham density functional simulations and how to bypass themIn high temperature density functional theory simulations (from tens of eV to keV) the total number of Kohn-Sham orbitals is a critical quantity to get accurate results. To establish the relationship…Augustin Blanchet, Marc Torrent, Jean Clerouin·Aug 24, 2020SaveLearn
Optimized routines for event generators in QED-PIC codesIn recent years, the prospects of performing fundamental and applied studies at the next-generation high-intensity laser facilities have greatly stimulated the interest in performing large-scale…V. Volokitin, S. Bastrakov, A. Bashinov et al.·Aug 24, 2020SaveLearn
Numerical Quality Control for DFT-based Materials DatabasesElectronic-structure theory is a strong pillar of materials science. Many different computer codes that employ different approaches are used by the community to solve various scientific problems.…Christian Carbogno, Kristian Sommer Thygesen, Björn Bieniek et al.·Aug 24, 2020SaveLearn
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural NetworksThe Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficients for each case,…Xiaoli Chen, Liu Yang, Jinqiao Duan et al.·Aug 24, 2020SaveLearn
Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imagingWe present a novel adaptive machine-learning based approach for reconstructing three-dimensional (3D) crystals from coherent diffraction imaging (CDI). We represent the crystals using spherical…Alexander Scheinker, Reeju Pokharel·Aug 23, 2020SaveLearn
Machine learning potentials for multicomponent systems: The Ti-Al binary systemMachine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic…Atsuto Seko·Aug 22, 2020SaveLearn
Structure preserving algorithms for simulation of linearly damped acoustic systemsEnergy methods for constructing time-stepping algorithms are of increased interest in application to nonlinear problems, since numerical stability can be inferred from the conservation of the system…Vasileios Chatziioannou·Aug 21, 2020SaveLearn
Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural NetworksMagnetic reconnection is a fundamental process that quickly releases magnetic energy stored in a plasma.Identifying, from simulation outputs, where reconnection is taking place is non-trivial and, in…A. Hu, M. Sisti, F. Finelli et al.·Aug 21, 2020SaveLearn
Sparse Grids based Adaptive Noise Reduction strategy for Particle-In-Cell schemesWe propose a sparse grids based adaptive noise reduction strategy for electrostatic particle-in-cell (PIC) simulations. Our approach is based on the key idea of relying on sparse grids instead of a…Sriramkrishnan Muralikrishnan, Antoine J. Cerfon, Matthias Frey et al.·Aug 21, 2020SaveLearn