Simulation and Prediction of Countercurrent Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural NetworksThe application of Physics-Informed Neural Networks (PINNs) is investigated for the first time in solving the one-dimensional Countercurrent spontaneous imbibition (COUCSI) problem at both early and…Jassem Abbasi, Pål Østebø Andersen·May 6, 2023SaveLearn
Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter DistributionsIn optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing…Matthias Plock, Martin Hammerschmidt, Sven Burger et al.·May 4, 2023SaveLearn
Defect theory under steady illuminations and applicationsIllumination has been long known to affect semiconductor defect properties during either growth or operating process. Current theories of studying the illumination effects on defects usually have the…Guo-Jun Zhu, Yi-Bin Fang, Zhi-Guo Tao et al.·May 4, 2023SaveLearn
Baler -- Machine Learning Based Compression of Scientific DataStoring and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based…Fritjof Bengtsson, Caterina Doglioni, Per Alexander Ekman et al.·May 3, 2023SaveLearn
Shotgun crystal structure prediction using machine-learned formation energiesStable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this…Chang Liu, Hiromasa Tamaki, Tomoyasu Yokoyama et al.·May 3, 2023SaveLearn
Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting FlowsThis work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physics-informed clustering strategy centered on the K-means framework. The method allows for the injection of…Shivam Barwey, Venkat Raman·May 2, 2023SaveLearn
The muphyII Code: Multiphysics Plasma Simulation on Large HPC SystemsCollsionless astrophysical and space plasmas cover regions that typically display a separation of scales that exceeds any code's capabilities. To help address this problem, the muphyII code utilizes…Florian Allmann-Rahn, Simon Lautenbach, Magnus Deisenhofer et al.·May 2, 2023SaveLearn
Localization and spectrum of quasiparticles in a disordered fermionic Dicke modelWe study a fermionic two-band model with the interband transition resonantly coupled to a cavity. This model was recently proposed to explain cavity-enhanced charge transport, but a thorough…Sebastian Stumper, Junichi Okamoto·May 2, 2023SaveLearn
Invertible Coarse Graining with Physics-Informed Generative Artificial IntelligenceMultiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling,…Jun Zhang, Xiaohan Lin, Weinan E et al.·May 2, 2023SaveLearn
Quantum vibronic effects on the electronic properties of molecular crystalsWe present a study of molecular crystals, focused on the effect of nuclear quantum motion and anharmonicity on their electronic properties. We consider a system composed of relatively rigid…Arpan Kundu, Giulia Galli·Apr 26, 2023SaveLearn
MLIP-3: Active learning on atomic environments with Moment Tensor PotentialsNowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on sharing computer…Evgeny Podryabinkin, Kamil Garifullin, Alexander Shapeev et al.·Apr 25, 2023SaveLearn
MRADSIM-Converter: A new software for STEP to GDML conversionRadiation effects analysis of instruments operative in harsh radiation environment is crucial for performance and functionality of electronic devices and components. Engineering design of instruments…Ali Behcet Alpat, Abdullah Coban, Hakan Kaya et al.·Apr 25, 2023SaveLearn
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent OrganizationSpontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that…Adam Rupe, Karthik Kashinath, Nalini Kumar et al.·Apr 25, 2023SaveLearn
Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron TomographyMeasuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical…Jonathan Schwartz, Zichao Wendy Di, Yi Jiang et al.·Apr 24, 2023SaveLearn
A cell-centred Eulerian volume-of-fluid method for compressible multi-material flowsWe present a practical cell-centred volume-of-fluid method developed within a pure Eulerian setting for the simulation of compressible solid-fluid problems. The method builds on a previously…Timothy R. Law, Philip T. Barton·Apr 24, 2023SaveLearn
Diffusion-driven frictional aging in silicon carbideFriction is the force resisting relative motion of objects. The force depends on material properties, loading conditions and external factors such as temperature and humidity, but also contact aging…Even Marius Nordhagen, Henrik Andersen Sveinsson, Anders Malthe-Sørenssen·Apr 24, 2023SaveLearn
UKRmol-scripts: a Perl-based system for the automated operation of the photoionization and electron/positron scattering suite UKRmol+UKRmol-scripts is a set of Perl scripts to automatically run the UKRmol+ codes, a complex software suite based on the R-matrix method to calculate fixed-nuclei photoionization and electron- and…Karel Houfek, Jakub Benda, Zdeněk Mašín et al.·Apr 21, 2023SaveLearn
A High-Performance Implementation of Atomistic Spin Dynamics Simulations on x86 CPUsAtomistic spin dynamics simulations provide valuable information about the energy spectrum of magnetic materials in different phases, allowing one to identify instabilities and the nature of their…Hongwei Chen, Yujia Zhai, Joshua J. Turner et al.·Apr 21, 2023SaveLearn
Imposing Correct Jellium Response Is Key to Predict the Density Response by Orbital-Free DFTOrbital-free density functional theory (OF-DFT) constitutes a computationally highly effective tool for modeling electronic structures of systems ranging from room-temperature materials to warm dense…Zhandos A. Moldabekov, Xuecheng Shao, Michele Pavanello et al.·Apr 20, 2023SaveLearn
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic sizeThis work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combination of innovative…Albert Musaelian, Anders Johansson, Simon Batzner et al.·Apr 20, 2023SaveLearn
A 2D hybrid method for interfacial transport of passive scalarsA hybrid Eulerian-Lagrangian method is proposed to simulate passive scalar transport on arbitrary shape interface. In this method, interface deformation is tracked by an Eulerian method while the…Yu Fan, Yujie Zhu, Xiaoliang Li et al.·Apr 19, 2023SaveLearn
Construction of coarse-grained molecular dynamics with many-body non-Markovian memoryWe introduce a machine-learning-based coarse-grained molecular dynamics (CGMD) model that faithfully retains the many-body nature of the inter-molecular dissipative interactions. Unlike common…Liyao Lyu, Huan Lei·Apr 18, 2023SaveLearn
Comparison of effective and stable Langevin dynamics integratorsLangevin and Brownian simulations play a prominent role in computational research, and state of the art integration algorithms provide trajectories with different stability ranges and accuracy in…Bogdan Tanygin, Simone Melchionna·Apr 18, 2023SaveLearn
Solving stiff ordinary differential equations using physics informed neural networks (PINNs): simple recipes to improve training of vanilla-PINNsPhysics informed neural networks (PINNs) are nowadays used as efficient machine learning methods for solving differential equations. However, vanilla-PINNs fail to learn complex problems as ones…Hubert Baty·Apr 17, 2023SaveLearn
Equivariant Tensor Network PotentialsMachine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs' ability of…Max Hodapp, Alexander Shapeev·Apr 17, 2023SaveLearn