Nanoporous C3N4, C3N5 and C3N6 nanosheets; Novel strong semiconductors with low thermal conductivities and appealing optical/electronic propertiesCarbon nitride two-dimensional (2D) materials are among the most attractive class of nanomaterials, with wide range of application prospects. As a continuous progress, most recently, two novel carbon…Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi et al.·Jun 6, 2020SaveLearn
Dependence of energy barrier reduction on collective excitations in square artificial spin ice: A comprehensive comparison of simulation techniquesWe perform micromagnetic simulations to study the switching barriers in square artificial spin ice systems consisting of elongated single domain magnetic islands arranged on a square lattice. By…Sabri Koraltan, Matteo Pancaldi, Naëmi Leo et al.·Jun 6, 2020SaveLearn
Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy dataDynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic…Maxim Ziatdinov, Shuai Zhang, Orion Dollar et al.·Jun 5, 2020SaveLearn
Accurate and efficient calculation of photoionization in streamer discharges using fast multipole methodThis paper focuses on the three-dimensional simulation of the photoionization in streamer discharges, and provides a general framework to efficiently and accurately calculate the photoionization…Bo Lin, Chijie Zhuang, Zhenning Cai et al.·Jun 5, 2020SaveLearn
Wall modeled immersed boundary method for high Reynolds number flow over complex terrainModeling the effect of complex terrain on high Reynolds number flows is important to improve our understanding of flow dynamics in wind farms and the dispersion of pollen and pollutants in hilly or…Luoqin Liu, Richard J. A. M. Stevens·Jun 5, 2020SaveLearn
Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductorsIt has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using ab initio molecular dynamics (AIMD) due to its high computational cost.…Jianxing Huang, Linfeng Zhang, Han Wang et al.·Jun 5, 2020SaveLearn
Real-space formulation of the stress tensor for O(N) density functional theory: application to high temperature calculationsWe present an accurate and efficient real-space formulation of the Hellmann-Feynman stress tensor for O(N) Kohn-Sham density functional theory (DFT). While applicable at any temperature,…Abhiraj Sharma, Sebastien Hamel, Mandy Bethkenhagen et al.·Jun 5, 2020SaveLearn
Active Learning A Neural Network Model For Gold Clusters \& Bulk From Sparse First Principles Training DataSmall metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the…Troy D Loeffler, Sukriti Manna, Tarak K Patra et al.·Jun 5, 2020SaveLearn
Benchmarking of Numerical Models for Wave Overtopping at Dikes with Shallow Mildly Sloping Foreshores: Accuracy versus SpeedTo accurately predict the consequences of nearshore waves, coastal engineers often employ numerical models. A variety of these models, broadly classified as either phase-resolving or phase-averaged,…Christopher Lashley, Barbara Zanuttigh, Jeremy Bricker et al.·Jun 5, 2020SaveLearn
Ensuring 'well-balanced' shallow water flows via a discontinuous Galerkin finite element method: issues at lowest orderThe discontinuous Galerkin finite element method (DGFEM) developed by Rhebergen et al. (2008) offers a robust method for solving systems of nonconservative hyperbolic partial differential equations…Thomas Kent, Onno Bokhove·Jun 5, 2020SaveLearn
A stochastic kinetic scheme for multi-scale plasma transport with uncertainty quantificationIn this paper, a physics-oriented stochastic kinetic scheme will be developed that includes random inputs from both flow and electromagnetic fields via a hybridization of stochastic Galerkin and…Tianbai Xiao, Martin Frank·Jun 4, 2020SaveLearn
Heterogeneous Multi-Rate mass transfer models in OpenFOAMWe implement the Multi-Rate Mass Transfer (MRMT) model for mobile-immobile transport in porous media within the open-source finite volume library OpenFOAM Foundation2014. Unlike…Federico Municchi, Nicodemo di Pasquale, Marco Dentz et al.·Jun 4, 2020SaveLearn
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event ProbabilitiesWe develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excursion probability…Vishwas Rao, Romit Maulik, Emil Constantinescu et al.·Jun 4, 2020SaveLearn
Integrating Machine Learning with Physics-Based ModelingMachine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. However, many issues need to be addressed before this becomes a reality.…Weinan E, Jiequn Han, Linfeng Zhang·Jun 4, 2020SaveLearn
A multiscale porous--resolved methodology for efficient simulation of heat and fluid transport in complex geometries, with application to electric power transformersThe numerical simulation of fluid flow through a complex geometry with heat transfer is of strong interest for many applications, such as oil-filled power transformers. A fundamental challenge here…Ole H. H. Meyer, Karl Yngve Lervåg, Åsmund Ervik·Jun 3, 2020SaveLearn
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic KernelsA majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide…Marcus M. Noack, Gregory S. Doerk, Ruipeng Li et al.·Jun 3, 2020SaveLearn
Temperature and its control in molecular dynamics simulationsThe earliest molecular dynamics simulations relied on solving the Newtonian or equivalently the Hamiltonian equations of motion for a system. While pedagogically very important as the total energy is…M Sri Harish, Puneet Kumar Patra·Jun 3, 2020SaveLearn
Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-term Precipitation ForecastingWith the accumulation of meteorological big data, data-driven models for short-term precipitation forecasting have shown increasing promise. We focus on Koopman operator analysis, which is a…Shitao Zheng, Takashi Miyamoto, Koyuru Iwanami et al.·Jun 3, 2020SaveLearn
Deep learning-based reduced order models in cardiac electrophysiologyPredicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the formulation and numerical approximation of coupled nonlinear dynamical systems. These…Stefania Fresca, Andrea Manzoni, Luca Dedè et al.·Jun 2, 2020SaveLearn
On the simulation of multicomponent and multiphase compressible flowsThe following paper presents two simulation strategies for compressible two-phase or multicomponent flows. One is a full non-equilibrium model in which the pressure and velocity are driven towards…Rémi Abgrall, Paola Bacigaluppi, Barbara Re·Jun 2, 2020SaveLearn
Modeling non-Fickian solute transport due to mass transfer and physical heterogeneity on arbitrary groundwater velocity fieldsWe present a hybrid approach to groundwater transport modeling, "CTRW-on-a-streamline", that allows continuous-time random walk (CTRW) particle tracking on large-scale, explicitly-delineated…Scott K. Hansen, Brian Berkowitz·Jun 2, 2020SaveLearn
Identification of hydrodynamic instability by convolutional neural networksThe onset of hydrodynamic instabilities is of great importance in both industry and daily life, due to the dramatic mechanical and thermodynamic changes for different types of flow motions. In this…Wuyue Yang, Liangrong Peng, Yi Zhu et al.·Jun 2, 2020SaveLearn
Clifford boundary conditions: a simple direct-sum evaluation of Madelung constantsWe propose a simple direct-sum method for the efficient evaluation of lattice sums in periodic solids. It consists of two main principles: i) the creation of a supercell that has the topology of a…Nicolas Tavernier, Gian Luigi Bendazzoli, Véronique Brumas et al.·Jun 1, 2020SaveLearn
Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material PropertiesThe dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond interpolation of the training set to the prediction…Paul Sinz, Michael W. Swift, Xavier Brumwell et al.·Jun 1, 2020SaveLearn
Analog ensemble data assimilation and a method for constructing analogs with variational autoencodersIt is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of…Ian Grooms·Jun 1, 2020SaveLearn