GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulationsWe present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their…Zheyong Fan, Yanzhou Wang, Penghua Ying et al.·May 20, 2022SaveLearn
A fast dynamic smooth adaptive meshing scheme with applications to compressible flowWe develop a fast-running smooth adaptive meshing (SAM) algorithm for dynamic curvilinear mesh generation, which is based on a fast solution strategy of the time-dependent Monge-Amp\`ere (MA)…Raaghav Ramani, Steve Shkoller·May 19, 2022SaveLearn
ω-FWI: Robust full-waveform inversion with Fourier-based metricFull-waveform inversion is a cutting-edge methodology for recovering high-resolution subsurface models. However, one of the main conventional full-waveform optimization problems challenges is…Muhammad Izzatullah, Tariq Alkhalifah·May 18, 2022SaveLearn
Atomistic simulations of nanoindentation in single crystalline tungsten: The role of interatomic potentialsComputational modeling is usually applied to aid experimental exploration of advanced materials to better understand the fundamental plasticity mechanisms during mechanical testing. In this work, we…F. J. Dominguez-Gutierrez, P. Grigorev, A. Naghdi et al.·May 18, 2022SaveLearn
A new advance on dimensional-aware scalar, vector and matrix operations in C++We review the dimensional check problem of the high-level programming languages, discuss the existing solutions, and come up with a new solution suited for scientific and engineering computations.…Eduard George Stan, Dan Andrei Ciubotaru, Michele Renda et al.·May 18, 2022SaveLearn
Accelerated chemical space search using a quantum-inspired cluster expansion approachTo enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired…Hitarth Choubisa, Jehad Abed, Douglas Mendoza et al.·May 18, 2022SaveLearn
The BLUES function method for second-order partial differential equations: application to a nonlinear telegrapher equationAn analytic iteration sequence based on the extension of the BLUES (Beyond Linear Use of Equation Superposition) function method to partial differential equations (PDEs) with second-order time…Jonas Berx, Joseph O. Indekeu·May 18, 2022SaveLearn
A real-time TDDFT scheme for strong-field interaction in Cartesian coordinate gridIn this communication, we present a new approach towards RT-TDDFT through time-dependent KS equations based on an adiabatic eigenstate subspace (AES) procedure. It introduces a second-order…Abhisek Ghosal, Amlan K. Roy·May 18, 2022SaveLearn
Physics-Informed Machine Learning for Modeling Turbulence in SupernovaeTurbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS)…Platon I. Karpov, Chengkun Huang, Iskandar Sitdikov et al.·May 17, 2022SaveLearn
Deep learning density functionals for gradient descent optimizationMachine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via density functional…Emanuele Costa, Giuseppe Scriva, Rosario Fazio et al.·May 17, 2022SaveLearn
Using physics-informed neural networks to compute quasinormal modesIn recent years there has been an increased interest in neural networks, particularly with regard to their ability to approximate partial differential equations. In this regard, research has begun on…Alan S. Cornell, Anele Ncube, Gerhard Harmsen·May 17, 2022SaveLearn
The HEP Software Foundation CommunityThe HEP Software Foundation was founded in 2014 to tackle common problems of software development and sustainability for high-energy physics. In this paper we outline the motivation for the founding…Graeme A Stewart, Peter Elmer, Elizabeth Sexton-Kennedy·May 17, 2022SaveLearn
Spurious currents suppression by accurate difference schemes in multiphase lattice Boltzmann methodSpurious currents, which are often observed near a curved interface in the multiphase simulations by diffuse interface methods, are unphysical phenomena and usually damage the computational accuracy…Zhangrong Qin, Wenbo Chen, Chunyan Qin et al.·May 17, 2022SaveLearn
Ionic forces and stress tensor in all-electron DFT calculations using enriched finite element basisThe enriched finite element basis -- wherein the finite element basis is enriched with atom-centered numerical functions -- has recently been shown to be a computationally efficient basis for…Nelson D. Rufus, Vikram Gavini·May 15, 2022SaveLearn
An implicit, conservative and asymptotic-preserving electrostatic particle-in-cell algorithm for arbitrarily magnetized plasmas in uniform magnetic fieldsWe introduce a new electrostatic particle-in-cell algorithm capable of using large timesteps compared to particle gyro-period under a uniform external magnetic field. The algorithm extends earlier…Guangye Chen, Luis Chacón·May 13, 2022SaveLearn
Symmetry-breaking-induced multifunctionalities of two-dimensional chromium-based materials for nanoelectronics and clean energy conversionStructural symmetry-breaking that could lead to exotic physical properties plays a crucial role in determining the functions of a system, especially for two-dimensional (2D) materials. Here we…Lei Li, Tao Huang, Kun Liang et al.·May 13, 2022SaveLearn
Quantum Computing and Preconditioners for Hydrological Linear SystemsModeling hydrological fracture networks is a hallmark challenge in computational earth sciences. Accurately predicting critical features of fracture systems, e.g. percolation, can require solving…John Golden, Daniel O'Malley, Hari Viswanathan·May 12, 2022SaveLearn
Nuclear-Electronic Orbital Approach to Quantization of Protons in Periodic Electronic Structure CalculationsThe nuclear-electronic orbital (NEO) method is a well-established approach for treating nuclei quantum mechanically in molecular systems beyond the usual Born-Oppenheimer approximation. In this work,…Jianhang Xu, Ruiyi Zhou, Zhen Tao et al.·May 12, 2022SaveLearn
Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular WavefunctionsLeveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous…Kirill Shmilovich, Devin Willmott, Ivan Batalov et al.·May 12, 2022SaveLearn
Diatomic-py: A python module for calculating the rotational and hyperfine structure of 1 moleculesWe present a computer program to calculate the quantised rotational and hyperfine energy levels of 1 diatomic molecules in the presence of dc electric, dc magnetic, and off-resonant…Jacob A. Blackmore, Philip D. Gregory, Jeremy M. Hutson et al.·May 11, 2022SaveLearn
Models of Advance Recording Systems: A Multi-timescale Micromagnetic code for granular thin film magnetic recording systemsMicromagnetic modelling provides the ability to simulate large magnetic systems accurately without the computational cost limitation imposed by atomistic modelling. Through micromagnetic modelling it…Samuel Ewan Rannala, Andrea Meo, Sergiu Ruta et al.·May 11, 2022SaveLearn
Temporally coherent backmapping of molecular trajectories from coarse-grained to atomistic resolutionCoarse-graining offers a means to extend the achievable time and length scales of molecular dynamics simulations beyond what is practically possible in the atomistic regime. Sampling molecular…Kirill Shmilovich, Marc Stieffenhofer, Nicholas E. Charron et al.·May 11, 2022SaveLearn
Accelerating reactive-flow simulations using vectorized chemistry integrationThe high cost of chemistry integration is a significant computational bottleneck for realistic reactive-flow simulations using operator splitting. Here we present a methodology to accelerate the…Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung·May 10, 2022SaveLearn
Accelerating Real-Time Coupled Cluster Methods with Single-Precision Arithmetic and Adaptive Numerical IntegrationWe explore the framework of a real-time coupled cluster method with a focus on improving its computational efficiency. Propagation of the wave function via the time-dependent Schr\"odinger equation…Zhe Wang, Benjamin G. Peyton, T. Daniel Crawford·May 10, 2022SaveLearn
Data Reduction in Deterministic Neutron Transport Calculations Using Machine LearningNeutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This…Ben Whewell, Ryan G. McClarren·May 10, 2022SaveLearn