An assessment of the structural resolution of various fingerprints commonly used in machine learningAtomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many…Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen et al.·Aug 7, 2020SaveLearn
A Paradigm for Density Functional Theory Using Electron Distribution on the Energy CoordinateStatic correlation error(SCE) inevitably emerges when a dissociation of a covalent bond is described with a conventional denstiy-functional theory (DFT) for electrons. SCE gives rise to a serious…Hideaki Takahashi·Aug 7, 2020SaveLearn
Object classification in analytical chemistry via data-driven discovery of partial differential equationsGlycans are one of the most widely investigated biomolecules, due to their roles in numerous vital biological processes. This involvement makes it critical to understand their structure-function…J. L. Padgett, Y. Geldiyev, S. Gautam et al.·Aug 6, 2020SaveLearn
Multiscale Extended Finite Element Method for Deformable Fractured Porous MediaDeformable fractured porous media appear in many geoscience applications. While the extended finite element (XFEM) has been successfully developed within the computational mechanics community for…Fanxiang Xu, Hadi Hajibeygi, Lambertus J. Sluys·Aug 6, 2020SaveLearn
Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learningWe propose a scheme for ab initio configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs…Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi et al.·Aug 6, 2020SaveLearn
Approximate Reconstruction of Torsional Potential Energy Surface based on Voronoi TessellationTorsional modes within a complex molecule containing various functional groups are often strongly coupled so that the harmonic approximation and one-dimensional torsional treatment are inaccurate to…Chengming He, Yicheng Chi, Peng Zhang·Aug 6, 2020SaveLearn
Modeling and Simulation of Non-equilibrium Flows with Uncertainty QuantificationIn the study of gas dynamics, theoretical modeling and numerical simulation are mostly set up with deterministic settings. Given the coarse-grained modeling in theories of fluids, considerable…Tianbai Xiao·Aug 6, 2020SaveLearn
A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay predictionWe put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward…Shady Ahmed, Suraj Pawar, Omer San et al.·Aug 5, 2020SaveLearn
Protein Conformational States: A First Principles Bayesian MethodAutomated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the…David M. Rogers·Aug 5, 2020SaveLearn
Ab initio framework for systems with helical symmetry: theory, numerical implementation and applications to torsional deformations in nanostructuresWe formulate and implement Helical DFT -- a self-consistent first principles simulation method for nanostructures with helical symmetries. Such materials are well represented in all of…Amartya S. Banerjee·Aug 5, 2020SaveLearn
Meshless discretization of the discrete-ordinates transport equation with integration based on Voronoi cellsThe time-dependent radiation transport equation is discretized using the meshless-local Petrov-Galerkin method with reproducing kernels. The integration is performed using a Voronoi tessellation,…Brody R. Bassett, J. Michael Owen·Aug 5, 2020SaveLearn
Learning the constitutive relation of polymeric flows with memoryWe develop a learning strategy to infer the constitutive relation for the stress of polymeric flows with memory. We make no assumptions regarding the functional form of the constitutive relations,…Naoki Seryo, Takeshi Sato, John J. Molina et al.·Aug 5, 2020SaveLearn
Solving the acoustic VTI wave equation using physics-informed neural networksFrequency-domain wavefield solutions corresponding to the anisotropic acoustic wave equations can be used to describe the anisotropic nature of the earth. To solve a frequency-domain wave equation,…Chao Song, Tariq Alkhalifah, Umair bin Waheed·Aug 4, 2020SaveLearn
On the use of graph theory to interpret the output results from a Monte-Carlo depletion codeThe analysis of the results of a depletion code is often considered a tedious and delicate task for it requires both the processing of large volume of information (the time dependent composition of…Benjamin Dechenaux·Aug 4, 2020SaveLearn
Achieving thermodynamic consistency in a class of free-energy multiphase lattice Boltzmann modelsThe free-energy lattice Boltzmann (LB) model is one of the major multiphase models in the LB community. The present study is focused on a class of free-energy LB models in which the divergence of…Q. Li, Y. Yu, R. Z. Huang·Aug 4, 2020SaveLearn
Smoothed particle hydrodynamics with adaptive spatial resolution (SPH-ASR) for free surface flowsA numerical method based on smoothed particle hydrodynamics with adaptive spatial resolution (SPH-ASR) was developed for simulating free surface flows. This method can reduce the computational…Xiufeng Yang, Song-Charng Kong, Moubin Liu et al.·Aug 4, 2020SaveLearn
Adaptive stabilized finite elements: Continuation analysis of compaction banding in geomaterialsUnder compressive creep, visco-plastic solids experiencing internal mass transfer processes have been recently proposed to accommodate singular cnoidal wave solutions, as material instabilities at…Roberto J. Cier, Thomas Poulet, Sergio Rojas et al.·Aug 4, 2020SaveLearn
A Review on Machine Learning for Neutrino ExperimentsNeutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos…Fernanda Psihas, Micah Groh, Christopher Tunnell et al.·Aug 3, 2020SaveLearn
Efficient Modeling of Particle Transport through Aerosols in GEANT4We present a geometry class for efficiently simulating particle transport through aerosols in GEANT4. It is demonstrated that aerosol granularity can strongly affect this transport and thus a generic…Nathaniel J. L. MacFadden, Ara N. Knaian·Aug 3, 2020SaveLearn
Quantum Monte Carlo determination of the principal Hugoniot of deuteriumWe present Coupled Electron-Ion Monte Carlo results for the principal Hugoniot of deuterium together with an accurate study of the initial reference state of shock wave experiments. We discuss the…Michele Ruggeri, Markus Holzmann, David M. Ceperley et al.·Aug 1, 2020SaveLearn
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theoryWe propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this…Yixiao Chen, Linfeng Zhang, Han Wang et al.·Aug 1, 2020SaveLearn
A Polynomial Approach to the Spectrum of Dirac-Weyl Polygonal BilliardsThe Schr\"odinger equation in a square or rectangle with hard walls is solved in every introductory quantum mechanics course. Solutions for other polygonal enclosures only exist in a very restricted…M. F. C. Martins Quintela, J. M. B. Lopes dos Santos·Jul 31, 2020SaveLearn
Application of machine learning potentials to predict grain boundary properties in fcc elemental metalsAccurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study,…Takayuki Nishiyama, Atsuto Seko, Isao Tanaka·Jul 31, 2020SaveLearn
A variational interface-preserving and conservative phase-field method for the surface tension effect in two-phase flowsWe present a finite element based variational interface-preserving and conservative phase-field formulation for the modeling of incompressible two-phase flows with surface tension dynamics. The…Xiaoyu Mao, Vaibhav Joshi, Rajeev Jaiman·Jul 31, 2020SaveLearn
Solving inverse problems using conditional invertible neural networksInverse modeling for computing a high-dimensional spatially-varying property field from indirect sparse and noisy observations is a challenging problem. This is due to the complex physical system of…Govinda Anantha Padmanabha, Nicholas Zabaras·Jul 31, 2020SaveLearn