Reconstruction of Protein Structures from Single-Molecule Time SeriesSingle-molecule experimental techniques track the real-time dynamics of molecules by recording a small number of experimental observables. Following these observables provides a coarse-grained,…Maximilian Topel, Andrew L. Ferguson·Jul 30, 2020SaveLearn
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertaintiesA common bottleneck for materials discovery is synthesis. While recent methodological advances have resulted in major improvements in the ability to predicatively design novel materials, researchers…Shreshth A. Malik, Rhys E. A. Goodall, Alpha A. Lee·Jul 30, 2020SaveLearn
A genetic algorithm approach to reconstructing spectral content from filtered x-ray diode array spectrometersFiltered diode array spectrometers are routinely employed to infer the temporal evolution of spectral power from x-ray sources, but uniquely extracting spectral content from a finite set of broad,…G. E. Kemp, M. S. Rubery, C. D. Harris et al.·Jul 30, 2020SaveLearn
Dynamic properties of the warm dense electron gas: an ab initio path integral Monte Carlo approachThere is growing interest in warm dense matter (WDM) -- an exotic state on the border between condensed matter and plasmas. Due to the simultaneous importance of quantum and correlation effects WDM…Paul Hamann, Tobias Dornheim, Jan Vorberger et al.·Jul 30, 2020SaveLearn
Scalability Analysis of Direct and Iterative Solvers Used to Model Charging of Non-insulated Superconducting Pancake SolenoidsA mathematical model for the charging simulation of non-insulated superconducting pancake solenoids is presented. Numerical solutions are obtained by the simulation model implemented on the Petra-M…M. Mohebujjaman, S. Shiraiwa, B. LaBombard et al.·Jul 30, 2020SaveLearn
A Chebyshev-based High-order-accurate Integral Equation Solver for Maxwell's EquationsThis paper introduces a new method for discretizing and solving integral equation formulations of Maxwell's equations which achieves spectral accuracy for smooth surfaces. The approach is based on a…Jin Hu, Emmanuel Garza, Constantine Sideris·Jul 29, 2020SaveLearn
Kinetic modeling of multiphase flow based on simplified Enskog equationA new kinetic model for multiphase flow was presented under the framework of the discrete Boltzmann method (DBM). Significantly different from the previous DBM, a bottom-up approach was adopted in…Yudong Zhang, Aiguo Xu, Jingjiang Qiu et al.·Jul 29, 2020SaveLearn
Very high-order Cartesian-grid finite difference method on arbitrary geometriesAn arbitrary order finite difference method for curved boundary domains with Cartesian grid is proposed. The technique handles in a universal manner Dirichlet, Neumann or Robin condition. We…Stéphane Clain, Diogo Lopes, Rui Pereira·Jul 29, 2020SaveLearn
OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured gridsOpenSBLI is an open-source code-generation system for compressible fluid dynamics (CFD) on heterogeneous computing architectures. Written in Python, OpenSBLI is an explicit high-order…David J. Lusher, Satya P. Jammy, Neil D. Sandham·Jul 29, 2020SaveLearn
Generating a Machine-learned Equation of State for Fluid PropertiesEquations of State (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable…Kezheng Zhu, Erich A. Müller·Jul 29, 2020SaveLearn
A Multiscale Optimization Framework for Reconstructing Binary Images using Multilevel PCA-based Control Space ReductionAn efficient computational approach for optimal reconstructing parameters of binary-type physical properties for models in biomedical applications is developed and validated. The methodology includes…Priscilla M. Koolman, Vladislav Bukshtynov·Jul 28, 2020SaveLearn
Code-Verification Techniques for Hypersonic Reacting Flows in Thermochemical NonequilibriumThe study of hypersonic flows and their underlying aerothermochemical reactions is particularly important in the design and analysis of vehicles exiting and reentering Earth's atmosphere.…Brian A. Freno, Brian R. Carnes, V. Gregory Weirs·Jul 28, 2020SaveLearn
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networksComputer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large…Jurgis Ruza, Wujie Wang, Daniel Schwalbe-Koda et al.·Jul 28, 2020SaveLearn
Real Space Orthogonal Projector-Augmented-Wave MethodThe projector augmented wave (PAW) method of Bl\"ochl makes smooth but non-orthogonal orbitals. Here we show how to make PAW orthogonal, using a cheap transformation of the wave-functions. We show…Wenfei Li, Daniel Neuhauser·Jul 28, 2020SaveLearn
Variationally Derived Intermediates for Correlated Free Energy Estimates between Intermediate StatesFree energy difference calculations based on atomistic simulations generally improve in accuracy when sampling from a sequence of intermediate equilibrium thermodynamic states that bridge the…Martin Reinhardt, Helmut Grubmüller·Jul 28, 2020SaveLearn
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architectureRecently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to…Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause et al.·Jul 28, 2020SaveLearn
An orthogonalization-free parallelizable framework for all-electron calculations in density functional theoryAll-electron calculations play an important role in density functional theory, in which improving computational efficiency is one of the most needed and challenging tasks. In the model formulations,…Bin Gao, Guanghui Hu, Yang Kuang et al.·Jul 28, 2020SaveLearn
Spin and charge distributions in Graphene/Nickel (111) substrate under Rashba spin-orbital couplingTo understand the coupling factor between Rashba spin-orbital interaction and ferromagnetic proximity effect, we design a Monte Carlo algorithm to simulate the spin and charge distributions for the…C. H. Wong, A. F. Zatsepin·Jul 28, 2020SaveLearn
A staggered-projection Godunov-type method for the Baer-Nunziato two-phase modelWhen describing the deflagration-to-detonation transition in solid granular explosives mixed with gaseous products of combustion, a well-developed two-phase mixture model is the compressible…Xin Lei, Jiequan Li·Jul 27, 2020SaveLearn
Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flowData assimilation in subsurface flow systems is challenging due to the large number of flow simulations often required, and by the need to preserve geological realism in the calibrated (posterior)…Meng Tang, Yimin Liu, Louis J. Durlofsky·Jul 27, 2020SaveLearn
Machine Learning Potential RepositoryThis paper introduces a machine learning potential repository that includes Pareto optimal machine learning potentials. It also shows the systematic development of accurate and fast machine learning…Atsuto Seko·Jul 27, 2020SaveLearn
Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network ModelThe application of machine learning methods to particle physics often doesn't provide enough understanding of the underlying physics. An interpretable model which provides a way to improve our…Marko Jercic, Nikola Poljak·Jul 26, 2020SaveLearn
A three-dimensional unified gas-kinetic wave-particle solver for flow computation in all regimesIn this paper, the unified gas-kinetic wave-particle (UGKWP) method has been constructed on three-dimensional unstructured mesh with parallel computing for multiscale flow simulation. Following the…Yipei Chen, Yajun Zhu, Kun Xu·Jul 26, 2020SaveLearn
PyXtal FF: a Python Library for Automated Force Field GenerationWe present PyXtal FF, a package based on Python programming language, for developing machine learning potentials (MLPs). The aim of PyXtal FF is to promote the application of atomistic simulations by…Howard Yanxon, David Zagaceta, Binh Tang et al.·Jul 25, 2020SaveLearn
Learning Variational Data Assimilation Models and SolversThis paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observations, possibly noisy…Ronan Fablet, Bertrand Chapron, Lucas. Drumetz et al.·Jul 25, 2020SaveLearn