IRNet: A General Purpose Deep Residual Regression Framework for Materials DiscoveryMaterials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning…Dipendra Jha, Logan Ward, Zijiang Yang et al.·Jul 7, 2019SaveLearn
PANNA: Properties from Artificial Neural Network ArchitecturesPrediction of material properties from first principles is often a computationally expensive task. Recently, artificial neural networks and other machine learning approaches have been successfully…Ruggero Lot, Franco Pellegrini, Yusuf Shaidu et al.·Jul 6, 2019SaveLearn
Fast optical absorption spectra calculations for periodic solid state systemsWe present a method to construct an efficient approximation to the bare exchange and screened direct interaction kernels of the Bethe-Salpeter Hamiltonian for periodic solid state systems via the…F. Henneke, L. Lin, C. Vorwerk et al.·Jul 5, 2019SaveLearn
Large scale ab-initio simulations of dislocationsWe present a novel methodology to compute relaxed dislocations core configurations, and their energies in crystalline metallic materials using large-scale ab-intio simulations. The approach is…Mauricio Ponga, Kaushik Bhattacharya, Michael Ortiz·Jul 4, 2019SaveLearn
Data-Centric Mixed-Variable Bayesian Optimization For Materials DesignMaterials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence…Akshay Iyer, Yichi Zhang, Aditya Prasad et al.·Jul 4, 2019SaveLearn
An FE-EBC Method for Electromagnetic Scattering from Inhomogeneous ObjectsIn this paper, we present a finite-element-extended boundary condition (FE-EBC) method for an efficient calculation of the electromagnetic wave scattering from inhomogeneous magneto-dielectric…Ehsan Khodapanah·Jul 4, 2019SaveLearn
Continuous and Optimally Complete Description of Chemical Environments Using Spherical Bessel DescriptorsRecently, machine learning potentials have been advanced as candidates to combine the high-accuracy of quantum mechanical simulations with the speed of classical interatomic potentials. A crucial…Emir Kocer, Jeremy K. Mason, Hakan Erturk·Jul 4, 2019SaveLearn
A Novel Approach to Describe Chemical Environments in High Dimensional Neural Network PotentialsA central concern of molecular dynamics simulations are the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system, and have…Emir Kocer, Jeremy K. Mason, Hakan Erturk·Jul 4, 2019SaveLearn
Second-order semi-implicit projection methods for micromagnetics simulationsMicromagnetics simulations require accurate approximation of the magnetization dynamics described by the Landau-Lifshitz-Gilbert equation, which is nonlinear, nonlocal, and has a non-convex…Changjian Xie, Carlos J. García-Cervera, Cheng Wang et al.·Jul 4, 2019SaveLearn
Harmonic surface mapping algorithm for electrostatic potentials in an atomistic/continuum hybrid model for electrolyte solutionsSimulating charged many-body systems has been a computational demanding task due to the long-range nature of electrostatic interaction. For the multi-scale model of electrolytes which combines the…Jing Fu, Zecheng Gan·Jul 3, 2019SaveLearn
Numerical modelling of shock-bubble interactions using a pressure-based algorithm without Riemann solversThe interaction of a shock wave with a bubble features in many engineering and emerging technological applications, and has been used widely to test new numerical methods for compressible interfacial…Fabian Denner, Berend van Wachem·Jul 2, 2019SaveLearn
Evaluation of the Biot-Savart integral in electrostatic problems with non-uniform Dirichlet boundary conditionsWe present an analytical strategy to solve the electric field generated by a planar region A enclosed by a contour c which is kept with a fixed but non-uniform electric potential. The…Robert Salazar, Camilo Bayona, J. S. Solís Chaves·Jul 2, 2019SaveLearn
A hp-adaptive discontinuous Galerkin solver for elliptic equations in numerical relativityA considerable amount of attention has been given to discontinuous Galerkin methods for hyperbolic problems in numerical relativity, showing potential advantages of the methods in dealing with…Trevor Vincent, Harald P. Pfeiffer, Nils L. Fischer·Jul 2, 2019SaveLearn
An iterative scheme for the generalized Peierls-Nabarro model based on the inverse Hilbert transformA new semi-analytical iterative scheme is proposed in this work for solving the generalized Peierls-Nabarro model. The numerical method developed here exploits certain basic properties of the Hilbert…Amuthan A. Ramabathiran·Jul 2, 2019SaveLearn
A volume-of-fluid method for interface-resolved simulations of phase-changing two-fluid flowsWe present a numerical method for interface-resolved simulations of evaporating two-fluid flows based on the volume-of-fluid (VoF) method. The method has been implemented in an efficient FFT-based…Nicolò Scapin, Pedro Costa, Luca Brandt·Jul 1, 2019SaveLearn
Large and Controllable Spin-Valley Splitting in Two-Dimensional WS2/h-VN HeterostructureInspired by the profound physical connotations and potential application prospects of the valleytronics, we design a two-dimensional (2D) WS2/h-VN magnetic van der Waals (vdW) heterostructure and…Congming Ke, Yaping Wu, Weihuang Yang et al.·Jul 1, 2019SaveLearn
Eshelby tensors and overall properties of nano-composites considering both interface stretching and bending effectsIn this study, the fundamental framework of analytical micromechanics is generalized to consider nano-composites with both interface stretching and bending effects. The interior and exterior Eshelby…Junbo Wang, Peng Yan, Leiting Dong et al.·Jul 1, 2019SaveLearn
Semiclassical vibrational spectroscopy with Hessian databasesWe report on a new approach to ease the computational overhead of ab initio on-the-fly semiclassical dynamics simulations for vibrational spectroscopy. The well known bottleneck of such computations…Riccardo Conte, Fabio Gabas, Giacomo Botti et al.·Jul 1, 2019SaveLearn
Discrete effect on the anti-bounce-back boundary condition of lattice Bhatnagar-Gross-Krook model for convection-diffusion equationsThe discrete effect on the boundary condition has been a fundamental topic for the lattice Boltzmann method in simulating heat and mass transfer problems. In previous works based on the halfway…Liang Wang, Xuhui Meng, Hao-Chi Wu et al.·Jul 1, 2019SaveLearn
Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machinesThe projective quantum Monte Carlo (PQMC) algorithms are among the most powerful computational techniques to simulate the ground state properties of quantum many-body systems. However, they are…S. Pilati, E. M. Inack, P. Pieri·Jul 1, 2019SaveLearn
Electronic and optical properties of Germagraphene, a direct band-gap semiconductorIn this communication, we report a theoretical attempt to understand the electronic and optical properties of germagraphene, a two-dimensional graphene analogue. We study two different structures,…Sujoy Datta, Debnarayan Jana, Chhanda Basu Chaudhuri et al.·Jul 1, 2019SaveLearn
Spatial population dynamics: beyond the Kirkwood superposition approximation by advancing to the Fisher-Kopeliovich ansatzThe superior Fisher-Kopeliovich closure is applied to the hierarchy of master equations for spatial moments of population dynamics for the first time. As a consequence, the population density, pair…Igor Omelyan·Jun 29, 2019SaveLearn
SCALAR: an AMR code to simulate axion-like dark matter modelsWe present a new code, SCALAR, based on the high-resolution hydrodynamics and N-body code RAMSES, to solve the Schrödinger equation on adaptive refined meshes. The code is intended to be used to…Mattia Mina, David F. Mota, Hans A. Winther·Jun 28, 2019SaveLearn
Determining Free Energy Differences Through Variational MorphingFree energy calculations based on atomistic Hamiltonians and sampling are key to a first principles understanding of biomolecular processes, material properties, and macromolecular chemistry. Here,…Martin Reinhardt, Helmut Grubmüller·Jun 28, 2019SaveLearn
TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice modelsWe use TensorNetwork [C. Roberts et al., arXiv: 1905.01330], a recently developed API for performing tensor network contractions using accelerated backends such as TensorFlow, to implement an…Martin Ganahl, Ashley Milsted, Stefan Leichenauer et al.·Jun 28, 2019SaveLearn