Algorithm for branching and population control in correlated samplingCorrelated sampling has wide-ranging applications in Monte Carlo calculations. When branching random walks are involved, as commonly found in many algorithms in quantum physics and electronic…Siyuan Chen, Yiqi Yang, Miguel Morales et al.·Jul 27, 2023SaveLearn
Pink-noise dynamics in an evolutionary game on a regular graphWe consider an iterated multiplayer prisoner's dilemma game on a square lattice and regular graphs based on the pairwise-Fermi update rule, and obtain heat-maps of the fraction of cooperators and the…Yuki Sakamoto, Masahito Ueda·Jul 26, 2023SaveLearn
CH4 and CO2 Adsorption Mechanisms on Monolayer Graphenylene and their Effects on Optical and Electronic PropertiesIn this study, we employ a computational chemistry-based modeling approach to investigate the adsorption mechanisms of CH4 and CO2 on monolayer GPNL, with a specific focus on their effects on…A. Aligayev, F. J. Dominguez-Gutierrez, M. Chourashiya et al.·Jul 25, 2023SaveLearn
Finding discrete symmetry groups via Machine LearningWe introduce a machine-learning approach (denoted Symmetry Seeker Neural Network) capable of automatically discovering discrete symmetry groups in physical systems. This method identifies the finite…Pablo Calvo-Barlés, Sergio G. Rodrigo, Eduardo Sánchez-Burillo et al.·Jul 25, 2023SaveLearn
Calculating and resumming the classical virial expansion using automated algebraUsing schematic model potentials, we calculate exactly the virial coefficients of a classical gas up to sixth order and use them to assess the convergence properties of the virial expansion of basic…Aaron M. Miller, Joaquín E. Drut·Jul 24, 2023SaveLearn
Synthetic pre-training for neural-network interatomic potentialsMachine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of…John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer·Jul 24, 2023SaveLearn
Predicting Coupled Electron and Phonon Transport Using Steepest-Entropy-Ascent Quantum ThermodynamicsThe principal paradigm for determining the thermoelectric properties of materials is based on the Boltzmann transport equations (BTEs) or Landauer equivalent. These equations depend on the electron…J. A. Worden, M. R. von Spakovsky, C. Hin·Jul 24, 2023SaveLearn
On the use of mixed potential formulation for finite-element analysis of large-scale magnetization problems with large memory demandThe finite-element analysis of three-dimensional magnetostatic problems in terms of magnetic vector potential has proven to be one of the most efficient tools capable of providing the excellent…Alexander Chervyakov·Jul 23, 2023SaveLearn
Electronic properties of carbon nanostructures based on bipartite nanocages unitsWe use first principles simulations to investigate the electronic properties of a set of carbon nanocages with a bipartite structure. These nanocages are exclusively formed by hexagonal and…Fábio Nascimento de Sousa, Divino Eliaquino, Fabrício Morais de Vasconcelos e Eduardo Costa Girão·Jul 22, 2023SaveLearn
Sub-quadratic scaling real-space random-phase approximation correlation energy calculations for periodic systems with numerical atomic orbitalsThe random phase approximation (RPA) as formulated as an orbital-dependent, fifth-rung functional within the density functional theory (DFT) framework offers a promising approach for calculating the…Rong Shi, Peize Lin, Min-Ye Zhang et al.·Jul 22, 2023SaveLearn
A high-order finite volume method for Maxwell's equations in heterogeneous and time-varying mediaWe develop a finite volume method for Maxwell's equations in materials whose electromagnetic properties vary in space and time. We investigate both conservative and non-conservative numerical…Damian P. San Roman Alerigi, David I. Ketcheson, Boon S. Ooi·Jul 21, 2023SaveLearn
Solving differential equations with Deep Learning: a beginner's guideThe research in Artificial Intelligence methods with potential applications in science has become an essential task in the scientific community last years. Physics Informed Neural Networks (PINNs) is…Luis Medrano Navarro, Luis Martín Moreno, Sergio G Rodrigo·Jul 20, 2023SaveLearn
Dynamical and statistical properties of estimated high-dimensional ODE models: The case of the Lorenz'05 type II modelThe performance of estimated models is often evaluated in terms of their predictive capability. In this study, we investigate another important aspect of estimated model evaluation: the disparity…Aljaz Pavšek, Martin Horvat, Jus Kocijan·Jul 18, 2023SaveLearn
Three-dimensional analysis of vortex-lattice formation in rotating Bose-Einstein condensates using smoothed-particle hydrodynamicsRecently, we presented a new numerical scheme for vortex lattice formation in a rotating Bose-Einstein condensate (BEC) using smoothed particle hydrodynamics (SPH) with an explicit time-integrating…Satori Tsuzuki, Eri Itoh, Katsuhiro Nishinari·Jul 17, 2023SaveLearn
Reducing hyperparameter dependence by external timescale tailoringTask specific hyperparameter tuning in reservoir computing is an open issue, and is of particular relevance for hardware implemented reservoirs. We investigate the influence of directly including…Lina C. Jaurigue, Kathy Lüdge·Jul 17, 2023SaveLearn
Multi-fidelity experimental design for ice-sheet simulationComputer simulations are becoming an essential tool in many scientific fields from molecular dynamics to aeronautics. In glaciology, future predictions of sea level change require input from ice…Pierre Thodoroff, Markus Kaiser, Rosie Williams et al.·Jul 17, 2023SaveLearn
Scan Coil Dynamics Simulation for Subsampled Scanning Transmission Electron MicroscopySubsampling and fast scanning in the scanning transmission electron microscope is problematic due to scan coil hysteresis - the mismatch between the actual and assumed location of the electron probe…Daniel Nicholls, Jack Wells, Alex W. Robinson et al.·Jul 17, 2023SaveLearn
Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte CarloNeural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches…Ruichen Li, Haotian Ye, Du Jiang et al.·Jul 17, 2023SaveLearn
Solar Cells, Lambert W and the LogWright FunctionsAlgorithms that calculate the current-voltage (I-V) characteristics of a solar cell play an important role in processes that aim to improve the efficiency of a solar cell. I-V characteristics can be…Prabhat Lankireddy, Sibibalan Jeevanandam, Aditya Chaudhary et al.·Jul 16, 2023SaveLearn
Reproducibility of density functional approximations: how new functionals should be reportedDensity functional theory is the workhorse of chemistry and materials science, and novel density functional approximations (DFAs) are published every year. To become available in program packages,…Susi Lehtola, Miguel A. L. Marques·Jul 14, 2023SaveLearn
Solving higher-order Lane-Emden-Fowler type equations using physics-informed neural networks: benchmark tests comparing soft and hard constraintsIn this paper, numerical methods using Physics-Informed Neural Networks (PINNs) are presented with the aim to solve higher-order ordinary differential equations (ODEs). Indeed, this deep-learning…Hubert Baty·Jul 14, 2023SaveLearn
Gas-Kinetic Scheme for Partially Ionized Plasma in Hydrodynamic RegimeMost plasmas are only partially ionized. To better understand the dynamics of these plasmas, the behaviors of a mixture of neutral species and plasma in ideal magnetohydrodynamic states are…Zhigang Pu, Chang Liu, Kun Xu·Jul 13, 2023SaveLearn
A quantum Monte Carlo algorithm for arbitrary spin-1/2 HamiltoniansWe present a universal parameter-free quantum Monte Carlo (QMC) algorithm designed to simulate arbitrary spin-1/2 Hamiltonians. To ensure the convergence of the Markov chain to equilibrium for…Lev Barash, Arman Babakhani, Itay Hen·Jul 13, 2023SaveLearn
Quantum computing for fluids: where do we stand?We present a pedagogical introduction to the current state of quantum computing algorithms for the simulation of classical fluids. Different strategies, along with their potential merits and…Sauro Succi, Wael Itani, Katepalli Sreenivasan et al.·Jul 11, 2023SaveLearn
Analytic pulse technique for computational electromagneticsNumerical modeling of electromagnetic waves is an important tool for understanding the interaction of light and matter, and lies at the core of computational electromagnetics. Traditional approaches…K. Weichman, K. G. Miller, B. Malaca et al.·Jul 10, 2023SaveLearn