A generalized hybrid method for surfactant dynamicsIn this paper, we develop a generalized hybrid method for both two-dimensional (2-D) and three-dimensional (3-D) surfactant dynamics. While the Navier-Stokes equations are solved by the Eulerian…Yu Fan, Shuoguo Zhang, Xiangyu Hu et al.·Mar 4, 2024SaveLearn
Ab initio path integral Monte Carlo simulations of warm dense two-component systems without fixed nodes: structural propertiesWe present extensive new ab initio path integral Monte Carlo (PIMC) results for a variety of structural properties of warm dense hydrogen and beryllium. To deal with the fermion sign problem…Tobias Dornheim, Sebastian Schwalbe, Maximilian Böhme et al.·Mar 4, 2024SaveLearn
Ab initio Wannier-representation-based calculations of photocurrent in semiconductors and metalsWe present a general ab initio method based on Wannier functions using the covariant derivative for simulating the photocurrent in solids. The method is widely applicable to charge/spin DC and AC…Junqing Xu, Haixiao Xiao·Mar 3, 2024SaveLearn
A genetic algorithm for the response of twisted nematic liquid crystals to an applied fieldWhen an external field is applied across a liquid-crystal cell, the twist and tilt distributions cannot be calculated analytically and must be extracted numerically. In the standard approach, the…Alicia Sit, Francesco Di Colandrea, Alessio D'Errico et al.·Mar 1, 2024SaveLearn
X marks the spot: accurate energies from intersecting extrapolations of continuum quantum Monte Carlo dataWe explore the application of an extrapolative method that yields very accurate total and relative energies from variational and diffusion quantum Monte Carlo (VMC and DMC) results. For a trial wave…Seyed Mohammadreza Hosseini, Ali Alavi, Pablo Lopez Rios·Mar 1, 2024SaveLearn
A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physicsI provide an introduction to the application of deep learning and neural networks for solving partial differential equations (PDEs). The approach, known as physics-informed neural networks (PINNs),…Hubert Baty·Mar 1, 2024SaveLearn
Extending the trapping theorem to provide local stability guarantees for quadratically nonlinear modelsThe Navier Stokes equations (NSEs) are partial differential equations (PDEs) to describe the nonlinear convective motion of fluids and they are computationally expensive to simulate because of their…Mai Peng, Alan Kaptanoglu, Chris Hansen et al.·Mar 1, 2024SaveLearn
Efficient simulations of Hartree--Fock equations by an accelerated gradient descent methodWe develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree--Fock equations for atoms interacting both with each other and with…Y. Ohno, A. Del Maestro, T. I. Lakoba·Feb 27, 2024SaveLearn
Low-light phase retrieval with implicit generative priorsPhase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for…Raunak Manekar, Elisa Negrini, Minh Pham et al.·Feb 27, 2024SaveLearn
Computing eigenfrequency sensitivities near exceptional pointsExceptional points are spectral degeneracies of non-Hermitian systems where both eigenfrequencies and eigenmodes coalesce. The eigenfrequency sensitivities near an exceptional point are significantly…Felix Binkowski, Julius Kullig, Fridtjof Betz et al.·Feb 27, 2024SaveLearn
Thermodynamics-informed super-resolution of scarce temporal dynamics dataWe present a method to increase the resolution of measurements of a physical system and subsequently predict its time evolution using thermodynamics-aware neural networks. Our method uses adversarial…Carlos Bermejo-Barbanoj, Beatriz Moya, Alberto Badías et al.·Feb 27, 2024SaveLearn
Generative diffusion model for surface structure discoveryWe present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked…Nikolaj Rønne, Alán Aspuru-Guzik, Bjørk Hammer·Feb 27, 2024SaveLearn
Beacon, a lightweight deep reinforcement learning benchmark library for flow controlRecently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the…Jonathan Viquerat, Philippe Meliga, Pablo Jeken et al.·Feb 27, 2024SaveLearn
Data-Driven Acceleration of Multi-Physics SimulationsMulti-physics simulations play a crucial role in understanding complex systems. However, their computational demands are often prohibitive due to high dimensionality and complex interactions, such…Stefan Meinecke, Malte Selig, Felix Köster et al.·Feb 26, 2024SaveLearn
JefiAtten: An Attention Based Neural Network Model for Solving Maxwell's Equations with Charge and Current SourcesWe present JefiAtten, a novel neural network model employing the attention mechanism to solve Maxwell's equations efficiently. JefiAtten uses self-attention and cross-attention modules to understand…Ming-Yan Sun, Peng Xu, Jun-Jie Zhang et al.·Feb 26, 2024SaveLearn
E(n)-Equivariant Cartesian Tensor Passing PotentialMachine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, message passing…Junjie Wang, Yong Wang, Haoting Zhang et al.·Feb 23, 2024SaveLearn
Automated Resonance Identification in Nuclear Data EvaluationGlobal and national efforts to deliver high-quality nuclear data to users have a broad impact across applications such as national security, reactor operation, basic science, medical fields, and…Noah A. W. Walton, Oleksii Zivenko, William Fritsch et al.·Feb 21, 2024SaveLearn
Data-Driven Forecasting of Non-Equilibrium Solid-State DynamicsWe present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality reduction stage with a…Stefan Meinecke, Felix Köster, Dominik Christiansen et al.·Feb 21, 2024SaveLearn
The Staggered Mesh Method: Accurate Exact Exchange towards the Thermodynamic Limit for SolidsIn periodic systems, the Hartree-Fock (HF) exchange energy exhibits the slowest convergence of all HF energy components as the system size approaches the thermodynamic limit. We demonstrate that the…Stephen Jon Quiton, Hamlin Wu, Xin Xing et al.·Feb 21, 2024SaveLearn
On-the-fly machine learned force fields for the study of warm dense matter: application to diffusion and viscosity of CHWe develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel…Shashikant Kumar, Xin Jing, John E. Pask et al.·Feb 21, 2024SaveLearn
Differentiability in Unrolled Training of Neural Physics Simulators on Transient DynamicsUnrolling training trajectories over time strongly influences the inference accuracy of neural network-augmented physics simulators. We analyze this in three variants of training neural…Bjoern List, Li-Wei Chen, Kartik Bali et al.·Feb 20, 2024SaveLearn
Surrogate models for vibrational entropy based on a spatial decompositionThe temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evaluating vibrational…Tina Torabi, Yangshuai Wang, Christoph Ortner·Feb 20, 2024SaveLearn
Second Order Meanfield Approximation for calculating Dynamics in Au-Nanoparticle NetworksExploiting physical processes for fast and energy-efficient computation bears great potential in the advancement of modern hardware components. This paper explores non-linear charge tunneling in…Evan Wonisch, Jonas Mensing, Andreas Heuer·Feb 19, 2024SaveLearn
Recent Extensions of the ZKCM Library for Parallel and Accurate MPS Simulation of Quantum CircuitsA C++ library ZKCM and its extension library ZKCMQC have been developed since 2011 for multiple-precision matrix computation and accurate matrix-product-state (MPS) quantum circuit simulation,…Akira SaiToh·Feb 19, 2024SaveLearn
Ab-initio investigation of hot electron transfer in CO2 plasmonic photocatalysis in presence of hydroxyl adsorbatePhotoreduction of carbon dioxide (CO2) on plasmonic structures is of great interest in photocatalysis to aid selectivity. While species commonly found in reaction environments and associated…Zelio Fusco, Dirk Koenig, Sean C. Smith et al.·Feb 18, 2024SaveLearn