On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal ApproximantsDespite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability…Fedor S. Avilov, Roman A. Eremin, Semen A. Budennyy et al.·Oct 2, 2024SaveLearn
Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural NetworksThe accurate modelling of structural dynamics is crucial across numerous engineering applications, such as Structural Health Monitoring (SHM), seismic analysis, and vibration control. Often, these…Marcus Haywood-Alexander, Giacomo Arcieri, Antonios Kamariotis et al.·Oct 2, 2024SaveLearn
First-principles computational methods for quantum defects in two-dimensional materials: A perspectiveQuantum defects are atomic defects in materials that provide resources to construct quantum information devices such as single-photon emitters (SPEs) and spin qubits. Recently, two-dimensional (2D)…Hosung Seo, Viktor Ivády, Yuan Ping·Oct 2, 2024SaveLearn
Adaptive sampling accelerates the hybrid deviational particle simulationsTo avoid ineffective collisions between the equilibrium states, the hybrid method with deviational particles (HDP) has been proposed to integrate the Vlasov-Poisson-Landau system, while leaving a new…Zhengyang Lei, Sihong Shao·Sep 29, 2024SaveLearn
Generalised tangent stabilised nonlinear elasticity: An automated framework for controlling material and geometric instabilitiesTangent stabilised large strain isotropic elasticity was recently proposed by Poya et al. [1] wherein by working directly with principal stretches the entire eigenstructure of constitutive and…Roman Poya, Rogelio Ortigosa, Antonio J. Gil et al.·Sep 26, 2024SaveLearn
High-Performance Implementation of the Optimized Event Generator for Strong-Field QED Plasma SimulationsNumerical simulation of strong-field quantum electrodynamics (SFQED) processes is an essential step towards current and future high-intensity laser experiments. The complexity of SFQED phenomena and…Elena Panova, Valentin Volokitin, Aleksei Bashinov et al.·Sep 25, 2024SaveLearn
Embedding an ANN-Based Crystal Plasticity Model into the Finite Element Framework using an ABAQUS User-Material SubroutineThis manuscript presents a practical method for incorporating trained Neural Networks (NNs) into the Finite Element (FE) framework using a user material (UMAT) subroutine. The work exemplifies…Yuqing He, Yousef Heider, Bernd Markert·Sep 25, 2024SaveLearn
Kinetically Consistent Coarse Graining using Kernel-based Extended Dynamic Mode DecompositionIn this paper, we show how kernel-based models for the Koopman generator -- the gEDMD method -- can be used to identify coarse-grained dynamics on reduced variables, which retain the slowest…Vahid Nateghi, Feliks Nüske·Sep 24, 2024SaveLearn
MGNN: Moment Graph Neural Network for Universal Molecular PotentialsThe quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has…Jian Chang, Shuze Zhu·Sep 24, 2024SaveLearn
A multi-dimensional, robust, and cell-centered finite-volume scheme for the ideal MHD equationsWe present a new multi-dimensional, robust, and cell-centered finite-volume scheme for the ideal MHD equations. This scheme relies on relaxation and splitting techniques and can be easily used at…Pascal Tremblin, Rémi Bourgeois, Solène Bulteau et al.·Sep 23, 2024SaveLearn
An Efficient Algorithm for Constrained CASSCF(1,2) and CASSCF(3,2) Simulations as Relevant to Electron and Hole Transfer ProblemsWe propose an efficient algorithm for the recently published electron/hole-transfer Dynamical-weighted State-averaged Constrained CASSCF (eDSC/hDSC) method studying charge transfer states and…Tian Qiu, Joseph E. Subotnik·Sep 22, 2024SaveLearn
Physics aware machine learning for micromagnetic energy minimization: recent algorithmic developmentsIn this work, we explore advanced machine learning techniques for minimizing Gibbs free energy in full 3D micromagnetic simulations. Building on Brown's bounds for magnetostatic self-energy, we…Sebastian Schaffer, Thomas Schrefl, Harald Oezelt et al.·Sep 19, 2024SaveLearn
Shifting sands of hardware and software in exascale quantum mechanical simulationsThe era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. Although the transition from petaflops to exascale computing has been…Ravindra Shinde, Claudia Filippi, Anthony Scemama et al.·Sep 18, 2024SaveLearn
Effects of the entropy source on Monte Carlo simulationsIn this paper we show how different sources of random numbers influence the outcomes of Monte Carlo simulations. We compare industry-standard pseudo-random number generators (PRNGs) to a quantum…Anton Lebedev, Annika Möslein, Olha I. Yaman et al.·Sep 17, 2024SaveLearn
On the generalization ability of coarse-grained molecular dynamics models for non-equilibrium processesOne essential goal of constructing coarse-grained molecular dynamics (CGMD) models is to accurately predict non-equilibrium processes beyond the atomistic scale. While a CG model can be constructed…Liyao Lyu, Huan Lei·Sep 17, 2024SaveLearn
Modeling a frustrated Ising square lattice with the D-Wave Quantum AnnealerThe Ising model with nearest-neighbor interactions on a two-dimensional (2D) square lattice is one of the simplest models for studying ferro-magnetic to para-magnetic transitions. Extensive results…C. Marin, A. Fontana, V. Bellani et al.·Sep 17, 2024SaveLearn
A modified recursive transfer matrix algorithm for radiation and scattering computation of a multilayer sphereWe discuss the electromagnetic scattering and radiation problems of multilayered spheres, reviewing the history of the Lorentz-Mie theory and the numerical stability issues encountered in handling…Jianing Zhang·Sep 17, 2024SaveLearn
A gradient flow model for ground state calculations in Wigner formalism based on density functional theoryIn this paper, a gradient flow model is proposed for conducting ground state calculations in Wigner formalism of many-body system in the framework of density functional theory. More specifically, an…Guanghui Hu, Ruo Li, Hongfei Zhan·Sep 17, 2024SaveLearn
Classification of electronic structures and state preparation for quantum computation of reaction chemistryQuantum computation for chemical problems will require the construction of guiding states with sufficient overlap with a target state. Since easily available and initializable mean-field states are…Maximilian Mörchen, Guang Hao Low, Thomas Weymuth et al.·Sep 13, 2024SaveLearn
Stochastic models of advection-diffusion in layered mediaMathematically modelling diffusive and advective transport of particles in heterogeneous layered media is important to many applications in computational, biological and medical physics. While…Elliot J. Carr·Sep 13, 2024SaveLearn
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine LearningExtreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a…Bo Liang, Hong Guo, Tianyu Zhao et al.·Sep 12, 2024SaveLearn
Logarithmically complex rigorous Fourier space solution to the 1D grating diffraction problemThe rigorous solution to the grating diffraction problem is a cornerstone step in many scientific fields and industrial applications ranging from the study of the fundamental properties of…Evgeniy Levdik, Alexey A. Shcherbakov·Sep 12, 2024SaveLearn
Descriptors-free Collective Variables From Geometric Graph Neural NetworksEnhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could…Jintu Zhang, Luigi Bonati, Enrico Trizio et al.·Sep 11, 2024SaveLearn
Active Learning for Discovering Complex Phase Diagrams with Gaussian ProcessesWe introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the next observation,…Max Zhu, Jian Yao, Marcus Mynatt et al.·Sep 11, 2024SaveLearn
nekCRF: A next generation high-order reactive low Mach flow solver for direct numerical simulationsExascale computing enables high-fidelity simulations of chemically reactive flows in practical geometries and conditions, and paves the way for valuable insights that can optimize combustion…Stefan Kerkemeier, Christos E. Frouzakis, Ananias G. Tomboulides et al.·Sep 10, 2024SaveLearn