Accurate Reduced Floating-Point Precision Implicit Monte CarloThis work describes methodologies to successfully implement the Implicit Monte Carlo (IMC) scheme for thermal radiative transfer in reduced-precision floating-point arithmetic. The methods used can…Simon Butson, Mathew Cleveland, Alex Long et al.·Jun 13, 2025SaveLearn
Physics-informed neural network framework for solving forward and inverse flexoelectric problemsFlexoelectricity, the coupling between strain gradients and electric polarization, poses significant computational challenges due to its governing fourth-order partial differential equations that…Hyeonbin Moon, Donggeun Park, Jinwook Yeo et al.·Jun 13, 2025SaveLearn
Suppressing spurious oscillations and particle noise in particle-in-cell simulationsParticle-in-cell (PIC) simulations are essential for studying kinetic plasma processes, but they often suffer from statistical noise, especially in plasmas with fast flows. We have also found that…Yuxi Chen, Hongyang Zhou, Gabor Toth·Jun 12, 2025SaveLearn
Distillation of atomistic foundation models across architectures and chemical domainsMachine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many…John L. A. Gardner, Daniel F. Thomas du Toit, Chiheb Ben Mahmoud et al.·Jun 12, 2025SaveLearn
How nanotextured interfaces influence the electronics in perovskite solar cellsPerovskite solar cells have reached power conversion efficiencies that rival those of established silicon photovoltaics. Nanotextures in perovskite solar cells scatter the incident light, thereby…Dilara Abdel, Jacob Relle, Thomas Kirchartz et al.·Jun 12, 2025SaveLearn
Phase-Space Engineering and Collective Dynamics in MemcomputingDigital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding ordinary…Chesson Sipling, Yuan-Hang Zhang, Massimiliano Di Ventra·Jun 11, 2025SaveLearn
Choosing a Suitable Acquisition Function for Batch Bayesian Optimization: Comparison of Serial and Monte Carlo ApproachesBatch Bayesian optimization is widely used for optimizing expensive experimental processes when several samples can be tested together to save time or cost. A central decision in designing a Bayesian…Imon Mia, Mark Lee, Weijie Xu et al.·Jun 11, 2025SaveLearn
Optimization of Sound Energy Reduction in the Polycarbonate Plate Reinforced with Carbon NanotubesIn order to develop a new method for sound insulation materials reinforcement, in this study, the effect of carbon nanotubes on polycarbonate plates as a sample of materials used in acoustic…Parinus Vedadi, Edris Faizabadi·Jun 11, 2025SaveLearn
Large-Scale Simulations of Turbulent Flows using Lattice Boltzmann Methods on Heterogeneous High Performance ComputersCurrent GPU-accelerated supercomputers promise to enable large-scale simulations of turbulent flows. Lattice Boltzmann Methods (LBM) are particularly well-suited to fulfilling this promise due to…Adrian Kummerländer, Fedor Bukreev, Yuji Shimojima et al.·Jun 11, 2025SaveLearn
Data-Driven Performance Measures using Global Properties of Attractors for Black-Box Surrogate Models of Chaotic SystemsIn climate systems, physiological models, optics, and many more, surrogate models are developed to reconstruct chaotic dynamical systems. We introduce four data-driven measures using global attractor…Luci Fumagalli, Kathy Lüdge, Jana de Wiljes et al.·Jun 11, 2025SaveLearn
Implicit unified gas kinetic particle method for steady-state solution of multiscale phonon transportThis paper presents a highly efficient implicit unified gas-kinetic particle (IUGKP) method for obtaining steady-state solutions of multi-scale phonon transport. The method adapts and reinterprets…Hongyu Liu, Xiaojian Yang, Chuang Zhang et al.·Jun 11, 2025SaveLearn
Unbiased higher-order frictional contact using midplane and patch based segment-to-segment penalty methodA highly accurate, single-pass, unbiased frictional contact algorithm for higher-order elements based on the concept of midplane is presented. Higher-order elements offer a lucrative choice for…Indrajeet Sahu, Nik Petrinic·Jun 10, 2025SaveLearn
Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solvingThe solution of partial differential equations (PDES) on irregular domains has long been a subject of significant research interest. In this work, we present an approach utilizing physics-informed…Cuizhi Zhou, Kaien Zhu·Jun 10, 2025SaveLearn
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy ResearchThe AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen interdisciplinary teams…Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald et al.·Jun 10, 2025SaveLearn
A unified fluid model for nonthermal plasmas and reacting flowsThis work presents a unified fluid modeling framework for reacting flows coupled with nonthermal plasmas (NTPs). Building upon the gas-plasma kinetics solver, ChemPlasKin, and the CFD library,…Xiao Shao, Deanna A. Lacoste, Hong G. Im·Jun 9, 2025SaveLearn
First-principles Quantum Insights into Bandgap Engineering, Valley Quantum Hall Effect, and Nonlinear Optical Response of Ge-Doped Graphene for Potential Optoelectronic ApplicationsThe valley in the band structure of materials has gained a lot of attention recently. The promising applications of the valley degree of freedom include the next-generation valleytronic devices,…Sana Maroof, Abdul Sattar, Azmat Iqbal Bashir et al.·Jun 9, 2025SaveLearn
A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated effectiveness across diverse atomistic systems but often require fine-tuning for task-specific accuracy. We investigate…Xiaoqing Liu, Kehan Zeng, Yangshuai Wang et al.·Jun 9, 2025SaveLearn
Efficient Collision Algorithms in DSMC for Rarefied Gas Dynamics: Markovian NTC-Pre-Scan and Bernoulli-Trial SchemesThe collision process is essential to the Direct Simulation Monte Carlo (DSMC) method, as it incorporates the fundamental principles of the Boltzmann and Kac stochastic equations. A series of…Ahmad Shoja-sani, Maryam Javani, Ehsan Roohi et al.·Jun 8, 2025SaveLearn
Nature of Hydrated Electron in Varied Solvation EnvironmentsUnderstanding the nature of solvated electrons is important in studying a range of chemical and biological phenomena. This study investigates the structural and dynamical behavior of an excess…Ritama Kar, Nisanth N. Nair·Jun 8, 2025SaveLearn
XtalOpt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto OptimizationVersion 14 of XtalOpt, an evolutionary multi-objective global optimization algorithm for crystal structure prediction, is now available for download from its official website…Samad Hajinazar, Eva Zurek·Jun 7, 2025SaveLearn
Addressing geometrical perturbations by applying generalized polynomial chaos to virtual density in continuous energy Monte-Carlo power iterationIn this work, we revisit the use of the virtual density method to model uniform geometrical perturbations. We propose a general algorithm in order to estimate explicitly the effect of geometrical…Théophile Bonnet, Anuj Dubey, Eugene Shwageraus·Jun 6, 2025SaveLearn
Revealing hidden correlations from complex spatial distributions: Adjacent Correlation AnalysisPhysics has been transforming our view of nature for centuries. While combining physical knowledge with computational approaches has enabled detailed modeling of physical systems' evolution,…Guang-Xing Li·Jun 6, 2025SaveLearn
Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discoveryThe development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step…Guang-Xing Li·Jun 6, 2025SaveLearn
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational costMachine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD)…Ilgar Baghishov, Jan Janssen, Graeme Henkelman et al.·Jun 6, 2025SaveLearn
Robustness of complexity estimation in event-driven signals against accuracy of event detection methodComplexity has gained recent attention in machine learning for its ability to extract synthetic information from large datasets. Complex dynamical systems are characterized by temporal complexity…Marco Cafiso, Paolo Paradisi·Jun 6, 2025SaveLearn