Graph-based Descriptors for Condensed MatterComputational scientists have long been developing a diverse portfolio of methodologies to characterise condensed matter systems. Most of the descriptors resulting from these efforts are ultimately…An Wang, Gabriele C. Sosso·Aug 12, 2024SaveLearn
Thermal Stoner-Wohlfarth Model for Magnetodynamics of Single Domain Nanoparticles: Implementation and ValidationWe present the thermal Stoner-Wohlfarth (tSW) model and apply it in the context of Molecular Dynamics simulations. The model is validated against an ensemble of immobilized, randomly oriented…Deniz Mostarac, Andrey A. Kuznetsov, Santiago Helbig et al.·Aug 12, 2024SaveLearn
SOFI: Finding point group symmetries in atomic clusters as finding the set of degenerate solutions in a shape-matching problemPoint Group (PG) symmetries play a fundamental role in many aspects of theoretical chemistry and computational materials science. With the objective to automatize the search of PG symmetry operations…Miha Gunde, Nicolas Salles, Luca Grisanti et al.·Aug 12, 2024SaveLearn
Simulating the dynamics of NV- formation in diamond in the presence of carbon self-interstitialsThis study utilises linear-scaling density functional theory (DFT) and develops a new machine-learning potential for carbon and nitrogen (GAP-CN), based on the carbon potential (GAP20), to…Guangzhao Chen, Joseph C. A. Prentice, Jason M. Smith·Aug 12, 2024SaveLearn
Kinetic representation of the unified gas-kinetic wave-particle method and beyondThe unified gas-kinetic wave-particle (UGKWP) method is a hybrid method for multiscale flow simulations, in which the contributions to the whole gas evolution from deterministic hydrodynamic wave and…Zhaoli Guo, Yajun Zhu, Kun Xu·Aug 11, 2024SaveLearn
Accelerating crystal structure search through active learning with neural networks for rapid relaxationsGlobal optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a…Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer et al.·Aug 7, 2024SaveLearn
Review of the finite difference Hartree-Fock method for atoms and diatomic molecules, and its implementation in the x2dhf programWe present an extensive review of the two-dimensional finite difference Hartree--Fock (FD HF) method, and present its implementation in the newest version of X2DHF, the FD HF program for atoms and…Jacek Kobus, Susi Lehtola·Aug 7, 2024SaveLearn
Carlo.jl: A general framework for Monte Carlo simulations in JuliaCarlo is a Monte Carlo simulation framework written in Julia. It provides MPI-parallel scheduling, organized storage of input, checkpoint, and output files, as well as statistical postprocessing.…Lukas Weber·Aug 6, 2024SaveLearn
A Workflow-Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure-Sensitive Mechanical DataFrom a data perspective, the materials mechanics field is characterized by sparsity of available data, mainly due to the strong microstructure-sensitivity of properties like strength, fracture…Ronak Shoghi, Alexander Hartmaier·Aug 6, 2024SaveLearn
Revisiting Shooting Point Monte Carlo Methods for Transition Path SamplingRare event sampling algorithms are essential for understanding processes that occur infrequently on the molecular scale, yet they are important for the long-time dynamics of complex molecular…Sebastian Falkner, Alessandro Coretti, Baron Peters et al.·Aug 6, 2024SaveLearn
G4CASCADE: A data-driven implementation of (n, γ) cascades in Geant4De-excitation γ cascades from neutron captures form a dominant background to MeV-scale signals. The Geant4 Monte Carlo simulation toolkit is widely used to model backgrounds in nuclear and…Leo Weimer, Emma Ellingwood, Otis Fischer et al.·Aug 5, 2024SaveLearn
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric ApplicationsThe added value of machine learning for weather and climate applications is measurable through performance metrics, but explaining it remains challenging, particularly for large deep learning models.…Tom Beucler, Arthur Grundner, Sara Shamekh et al.·Aug 4, 2024SaveLearn
Computational Self-Assembly of a Six-Fold Chiral QuasicrystalQuasicrystals are unique materials characterized by long-range order without periodicity. They are observed in systems such as metallic alloys, soft matter, and particle simulations. Unlike periodic…Nydia Roxana Varela-Rosales, Michael Engel·Aug 4, 2024SaveLearn
Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion ModelsThe rapid development of AI highlights the pressing need for sustainable energy, a critical global challenge for decades. Nuclear fusion, generally seen as an ultimate solution, has been the focus of…Chuan Liu, Chunshu Wu, Shihui Cao et al.·Aug 3, 2024SaveLearn
PLIC-Net: A Machine Learning Approach for 3D Interface Reconstruction in Volume of Fluid MethodsThe accurate reconstruction of immiscible fluid-fluid interfaces from the volume fraction field is a critical component of geometric Volume of Fluid (VOF) methods. A common strategy is the Piecewise…Andrew Cahaly, Fabien Evrard, Olivier Desjardins·Aug 2, 2024SaveLearn
Exact average many-body interatomic interaction model for random alloysUnderstanding the physical origin of deformation mechanisms in random alloys requires an understanding of their average behavior and, equally important, the role of local fluctuations around the…Max Hodapp·Aug 2, 2024SaveLearn
An iterative Monte Carlo method to solve nonlinear second-order differential equationsThe Monte Carlo method is a thriving and mathematically beautiful numerical technique used extensively, nowadays, to deal with many demanding problems in diverse fields. Here, we present an iterative…Martín Chávez-Páez, Enrique González-Tovar, Guillermo Iván Guerrero-García·Aug 1, 2024SaveLearn
Hyperparameter Optimization for Atomic Cluster Expansion PotentialsMachine-learning-based interatomic potentials enable accurate materials simulations on extended time- and lengthscales. ML potentials based on the Atomic Cluster Expansion (ACE) framework have…Daniel F. Thomas du Toit, Yuxing Zhou, Volker L. Deringer·Aug 1, 2024SaveLearn
Fast and scalable finite-element based approach for density functional theory calculations using projector-augmented wave methodIn this work, we present a computationally efficient methodology that utilizes a local real-space formulation of the projector augmented wave (PAW) method discretized with a finite-element (FE) basis…Kartick Ramakrishnan, Sambit Das, Phani Motamarri·Aug 1, 2024SaveLearn
Discovery of Green's function based on symbolic regression with physical hard constraintsThe Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications across various…Jianghang Gu, Mengge Du, Yuntian Chen et al.·Aug 1, 2024SaveLearn
Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials CharacterizationPhysical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate…Aditya Raghavan, Utkarsh Pratiush, Mani Valleti et al.·Aug 1, 2024SaveLearn
Multilevel Fast Multipole Algorithm for Electromagnetic Scattering by Large Metasurfaces using Static Mode RepresentationMetasurfaces, consisting of large arrays of interacting subwavelength scatterers, pose significant challenges for general-purpose computational methods due to their large electric dimensions and…Emanuele Corsaro, Giovanni Miano, Antonello Tamburrino et al.·Jul 31, 2024SaveLearn
Advanced Techniques for High-Performance Fock Matrix Construction on GPU ClustersThis Article presents two optimized multi-GPU algorithms for Fock matrix construction, building on the work of Ufimtsev et al. and Barca et al. The novel algorithms, opt-UM and opt-Brc, introduce…Elise Palethorpe, Ryan Stocks, Giuseppe M. J. Barca·Jul 31, 2024SaveLearn
BMach: a Bayesian machine for optimizing Hubbard U parameters in DFT+U with machine learningAccurately determining the effective Hubbard parameter (Ueff) in Density Functional Theory plus U (DFT+U) remains a significant challenge, often relying on empirical methods or linear response…Ritwik Das·Jul 30, 2024SaveLearn
Approximating electromagnetic fields in discontinuous media using a single physics-informed neural networkPhysics-Informed Neural Networks (PINNs) are a new family of numerical methods, based on deep learning, for modeling boundary value problems. They offer an advantage over traditional numerical…Michel Nohra, Steven Dufour·Jul 30, 2024SaveLearn