Atomistic insights into ultrafast SiGe nanoprocessingControlling ultrafast material transformations with atomic precision is essential for future nanotechnology. Pulsed laser annealing (LA), inducing extremely rapid and localized phase transitions, is…Gaetano Calogero, Domenica Raciti, Damiano Ricciarelli et al.·Sep 6, 2023SaveLearn
Enhancing the Stretchability of Two-Dimensional Materials through Kirigami: A Molecular Dynamics Study on Tungsten DisulfideIn recent years, the 'kirigami' technique has gained significant attention for creating meta-structures and meta-materials with exceptional characteristics, such as unprecedented stretchability.…K. Dey, S. Shahriar, M. A. R. Anan et al.·Sep 5, 2023SaveLearn
A call to arms: making the case for more reusable librariesThe traditional foundation of science lies on the cornerstones of theory and experiment. Theory is used to explain experiment, which in turn guides the development of theory. Since the advent of…Susi Lehtola·Sep 5, 2023SaveLearn
Ab initio uncertainty quantification in scattering analysis of microscopyEstimating parameters from data is a fundamental problem, customarily done by minimizing a loss function between a model and observed statistics. In scattering-based analysis, researchers often…Mengyang Gu, Yue He, Xubo Liu et al.·Sep 5, 2023SaveLearn
A fourth-order kernel for improving numerical accuracy and stability in Eulerian and total Lagrangian SPHThe error of smoothed particle hydrodynamics (SPH) using kernel for particle-based approximation mainly comes from smoothing and integration errors. The choice of kernels has a significant impact on…Zhentong Wang, Bo Zhang, Oskar J. Haidn et al.·Sep 4, 2023SaveLearn
TRIQS/Nevanlinna: Implementation of the Nevanlinna Analytic Continuation method for noise-free dataWe present the TRIQS/Nevanlinna analytic continuation package, an efficient implementation of the methods proposed by J. Fei et al in [Phys. Rev. Lett. 126, 056402 (2021)] and [Phys. Rev. B 104,…Sergei Iskakov, Alexander Hampel, Nils Wentzell et al.·Sep 4, 2023SaveLearn
Symbolically integrating tensor networks over various random tensors by the second version of Python RTNIWe are upgrading the Python-version of RTNI, which symbolically integrates tensor networks over the Haar-distributed unitary matrices. Now, PyRTNI2 can treat the Haar-distributed orthogonal matrices…Motohisa Fukuda·Sep 3, 2023SaveLearn
Physics-informed machine learning of the correlation functions in bulk fluidsThe Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models,…Wenqian Chen, Peiyuan Gao, Panos Stinis·Sep 2, 2023SaveLearn
Uncertainty Quantification on Spent Nuclear Fuel with LMCThe recently developed method Lasso Monte Carlo (LMC) for uncertainty quantification is applied to the characterisation of spent nuclear fuel. The propagation of nuclear data uncertainties to the…Arnau Albà, Andreas Adelmann, Dimitri Rochman·Sep 1, 2023SaveLearn
Data-driven Topology Optimization of Channel Flow ProblemsTypical topology optimization methods require complex iterative calculations, which cannot meet the requirements of fast computing applications. The neural network is studied to reduce the time of…Ce Guan, Jianyu Zhang, Zhen Li et al.·Sep 1, 2023SaveLearn
PyMoosh : a comprehensive numerical toolkit for computing the optical properties of multilayered structuresWe present PyMoosh, a Python-based simulation library designed to provide a comprehensive set of numerical tools allowing to compute essentially all optical characteristics of multilayered…Denis Langevin, Pauline Bennet, Abdourahman Khaireh-Walieh et al.·Sep 1, 2023SaveLearn
Accurate Computation of Quantum Excited States with Neural NetworksWe present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. The method has no…David Pfau, Simon Axelrod, Halvard Sutterud et al.·Aug 31, 2023SaveLearn
Implementing scalable matrix-vector products for the exact diagonalization methods in quantum many-body physicsExact diagonalization is a well-established method for simulating small quantum systems. Its applicability is limited by the exponential growth of the so-called Hamiltonian matrix that needs to be…Tom Westerhout, Bradford L. Chamberlain·Aug 31, 2023SaveLearn
High Performance GPU Accelerated MuST SoftwareThe MuST package is a computational software designed for ab initio electronic structure calculations for solids. The Locally Self-consistent Multiple Scattering (LSMS) method implemented in MuST…Xiao Liang, Edward Hanna, Derek Simmel et al.·Aug 30, 2023SaveLearn
A spectrum adaptive kernel polynomial methodThe kernel polynomial method (KPM) is a powerful numerical method for approximating spectral densities. Typical implementations of the KPM require an a prior estimate for an interval containing the…Tyler Chen·Aug 30, 2023SaveLearn
Full-scale ab initio simulations of laser-driven atomistic dynamicsThe coupling of excited states and ionic dynamics is the basic and challenging point for the materials response at extreme conditions. In laboratory, the intense laser produces transient nature and…Qiyu Zeng, Bo Chen, Shen Zhang et al.·Aug 26, 2023SaveLearn
Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmasThe Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance. Two examples are…Jai Kumar, David Zarzoso, Virginie Grandgirard et al.·Aug 23, 2023SaveLearn
Constrained Pressure-Temperature Residual (CPTR) Preconditioner Performance for Large-Scale Thermal CO2 Injection SimulationThis work studies the performance of a novel preconditioner, designed for thermal reservoir simulation cases and recently introduced in Roy et al. (2020) and Cremon et al. (2020), on large-scale…Matthias A. Cremon, Jacques Franc, Francois P. Hamon·Aug 23, 2023SaveLearn
MolSieve: A Progressive Visual Analytics System for Molecular Dynamics SimulationsMolecular Dynamics (MD) simulations are ubiquitous in cutting-edge physio-chemical research. They provide critical insights into how a physical system evolves over time given a model of interatomic…Rostyslav Hnatyshyn, Jieqiong Zhao, Danny Perez et al.·Aug 22, 2023SaveLearn
NIPG-DG schemes for transformed master equations modeling open quantum systemsThis work presents a numerical analysis of a Discontinuous Galerkin (DG) method for a transformed master equation modeling an open quantum system: a quantum sub-system interacting with a noisy…Jose A. Morales Escalante·Aug 22, 2023SaveLearn
Comprehensive Molecular Representation from Equivariant TransformerThe tradeoff between precision and performance in molecular simulations can nowadays be addressed by machine-learned force fields (MLFF), which combine ab initio accuracy with force field…Nianze Tao, Hiromi Morimoto, Stefano Leoni·Aug 21, 2023SaveLearn
Controllable Weyl nodes and Fermi arcs in a light-irradiated carbon allotropeThe precise control of Weyl physics in realistic materials oers a promising avenue to construct accessible topological quantum systems, and thus draw widespread attention in condensed-matter physics.…Ruoning Ji, Xianyong Ding, Fangyang Zhan et al.·Aug 21, 2023SaveLearn
Multiple scattering of 855 MeV electrons in amorphous and crystalline silicon: simulations versus experimentThe angular distribution function of multiple scattering experienced by 855 MeV electrons passing through an amorphous silicon plate and an oriented silicon crystal has been studied by means of…German Rojas-Lorenzo, Jesus Rubayo-Soneira, Maykel Marquez-Mijares et al.·Aug 21, 2023SaveLearn
Poisson quadrature method of moments for 2D kinetic equations with velocity of constant magnitudeThis work is concerned with kinetic equations with velocity of constant magnitude. We propose a quadrature method of moments based on the Poisson kernel, called Poisson-EQMOM. The derived moment…Yihong Chen, Qian Huang, Wen-An Yong et al.·Aug 19, 2023SaveLearn
Predicting Properties of Oxide Glasses Using Informed Neural NetworksMany modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine…Gregor Maier, Jan Hamaekers, Dominik-Sergio Martilotti et al.·Aug 18, 2023SaveLearn