Velocity-Inferred Hamiltonian Neural Networks: Learning Energy-Conserving Dynamics from Position-Only DataData-driven modeling of physical systems often relies on learning both positions and momenta to accurately capture Hamiltonian dynamics. However, in many practical scenarios, only position…Ruichen Xu, Zongyu Wu, Luoyao Chen et al.·May 5, 2025SaveLearn
Hyper Boris integrators for kinetic plasma simulationsWe propose a family of numerical solvers for the nonrelativistic Newton--Lorentz equation in kinetic plasma simulations. The new solvers extend the standard 4-step Boris procedure, which has…Seiji Zenitani, Tsunehiko N. Kato·May 4, 2025SaveLearn
Accelerated Integration of Stiff Reactive Systems Using Gradient-Informed Autoencoder and Neural Ordinary Differential EquationA combined autoencoder (AE) and neural ordinary differential equation (NODE) framework has been used as a data-driven reduced-order model for time integration of a stiff reacting system. In this…Mert Yakup Baykan, Vijayamanikandan Vijayarangan, Dong-hyuk Shin et al.·May 4, 2025SaveLearn
Polar Interpolants for Thin-Shell Microstructure HomogenizationThis paper introduces a new formulation for material homogenization of thin-shell microstructures. It addresses important challenges that limit the quality of previous approaches: methods that fit…Antoine Chan-Lock, Miguel Otaduy·May 3, 2025SaveLearn
Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you needThe promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limited by the accuracy…Mitchell Messerly, Sakib Matin, Alice E. A. Allen et al.·May 2, 2025SaveLearn
On Robust β-Spectra Shape Parameter ExtractionExperimental extraction of β-shape functions, C(W), is challenging. Comparing different experimental β-shapes to each other and to those predicted by theory in a consistent manner is…B. C. Rasco, T. Gray, T. Ruland·May 2, 2025SaveLearn
Exploring exponential time integration for strongly magnetized charged particle motionA fundamental task in particle-in-cell (PIC) simulations of plasma physics is solving for charged particle motion in electromagnetic fields. This problem is especially challenging when the plasma is…Tri P. Nguyen, Ilon Joseph, Mayya Tokman·May 2, 2025SaveLearn
QCMaquis 4.0: Multi-Purpose Electronic, Vibrational, and Vibronic Structure and Dynamics Calculations with the Density Matrix Renormalization GroupQCMaquis is a quantum chemistry software package for general molecular structure calculations in a matrix product state/matrix product operator formalism of the density matrix renormalization group…Kalman Szenes, Nina Glaser, Mihael Erakovic et al.·May 2, 2025SaveLearn
A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-time Dyson ExpansionTime-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on sub-femtosecond timescales. The interpretation of experimental…Cian Reeves, Michael Kurniawan, Yuanran Zhu et al.·May 1, 2025SaveLearn
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property predictionRecent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as…Junchi Liu, Ying Tang, Sergei Tretiak et al.·May 1, 2025SaveLearn
PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulationsMachine learning potential-driven molecular dynamics (MD) simulations have significantly enhanced the predictive accuracy of thermal transport properties across diverse materials. However, extracting…Ting Liang, Wenwu Jiang, Ke Xu et al.·May 1, 2025SaveLearn
Nystr\"om Type Exponential Integrators for Strongly Magnetized Charged Particle DynamicsSolving for charged particle motion in electromagnetic fields (i.e. the particle pushing problem) is a computationally intensive component of particle-in-cell (PIC) methods for plasma physics…Tri P. Nguyen, Ilon Joseph, Mayya Tokman·May 1, 2025SaveLearn
Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational BiophysicsIn computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are fundamentally…Yijie Xia, Xiaohan Lin, Zicheng Ma et al.·May 1, 2025SaveLearn
Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent PotentialsTwo-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions.…Wenwu Jiang, Hekai Bu, Ting Liang et al.·May 1, 2025SaveLearn
Coalescing MPI communication in 6D Vlasov simulations: solving ghost domains in VlasiatorHigh-performance computing is used for diverse simulations, some of which parallelize over the Message Passing Interface (MPI) with ease, whilst others may have challenges related to uniform…Markus Battarbee, Urs Ganse, Yann Pfau-Kempf et al.·Apr 30, 2025SaveLearn
A genetic algorithm to generate maximally orthogonal frames in complex spaceA frame is a generalization of a basis of a vector space to a redundant overspanning set whose vectors are linearly dependent. Frames find applications in signal processing and quantum information…Sebastián Roca-Jerat, Juan Román-Roche·Apr 29, 2025SaveLearn
Faster Random Walk-based Capacitance Extraction with Generalized Antithetic SamplingFloating random walk-based capacitance extraction has emerged in recent years as a tried and true approach for extracting parasitic capacitance in very large scale integrated circuits. Being a Monte…Periklis Liaskovitis, Marios Visvardis, Efthymios Efstathiou·Apr 29, 2025SaveLearn
LAMBench: A Benchmark for Large Atomistic ModelsLarge Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles…Anyang Peng, Chun Cai, Mingyu Guo et al.·Apr 28, 2025SaveLearn
Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce DataThe discovery of constitutive models for hyperelastic materials is essential yet challenging due to their nonlinear behavior and the limited availability of experimental data. Traditional methods…Hyeonbin Moon, Donggeun Park, Hanbin Cho et al.·Apr 28, 2025SaveLearn
PaRO-DeepONet: a particle-informed reduced-order deep operator network for Poisson solver in PIC simulationsParticle-in-Cell (PIC) simulations are widely used for modeling plasma kinetics by tracking discrete particle dynamics. However, their computational cost remains prohibitively high, due to the need…Jianhua Lv, Linlin Zhong·Apr 27, 2025SaveLearn
Supporting Higher-Order Interactions in Practical Ising MachinesIsing machines as hardware solvers of combinatorial optimization problems (COPs) can efficiently explore large solution spaces due to their inherent parallelism and physics-based dynamics. Many…Nafisa Sadaf Prova, Hüsrev Cılasun, Abhimanyu Kumar et al.·Apr 25, 2025SaveLearn
A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation DynamicsThe kinetics and dynamics of drug-protein binding and dissociation are crucial to understanding drug absorption and metabolism. Despite advances in artificial intelligence (AI) tools for drug-protein…Maodong Li, Jiying Zhang, Zhe Wang et al.·Apr 25, 2025SaveLearn
Optical to infrared mapping of vapor-to-liquid phase change dynamics using generative machine learningInfrared thermography is a powerful tool for studying liquid-to-vapor phase change processes. However, its application has been limited in the study of vapor-to-liquid phase transitions due to the…Siavash Khodakarami, Pouya Kabirzadeh, Chi Wang et al.·Apr 24, 2025SaveLearn
Implicit Sub-stepping Scheme for Critical State Soil ModelsThe stress integration of critical soil model is usually based on implicit Euler algorithm, where the stress predictor is corrected by employing a return mapping algorithm. In the case of large load…Hoang Giang Bui, Jelena Ninic, Günther Meschke·Apr 24, 2025SaveLearn
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraintsThis study focuses on the development of reinforcement learning based techniques for the design of microelectronic components under multiphysics constraints. While traditional design approaches based…Siddharth Nair, Timothy F. Walsh, Greg Pickrell et al.·Apr 23, 2025SaveLearn