🔬 Category

physics.comp-ph

1259 papers

Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.

On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.

Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.

Finite-Temperature Thermally-Assisted-Occupation Density Functional Theory, Ab Initio Molecular Dynamics, and Quantum Mechanics/Molecular Mechanics Methods

This paper proposes finite-temperature extensions of thermally-assisted-occupation density functional theory (FT-TAO-DFT) and its applications to ab initio molecular dynamics and QM/MM methods to investigate the thermal equilibrium properties of large multi-reference systems, demonstrating through n-acene studies that while electronic temperature effects are minor at moderate temperatures, nuclear temperature and environmental factors significantly influence radical nature and infrared spectra.

Shaozhi Li, Jeng-Da Chai2026-03-03⚛️ quant-ph

Two-Dimensional Kelvin-Helmholtz Instability with Anisotropic Pressure

This paper presents a comprehensive linear and numerical analysis of the two-dimensional Kelvin-Helmholtz instability in collisionless plasmas with anisotropic pressure, revealing that the magnetohydrodynamic limit yields significantly larger growth rates, current densities, and magnetic island formation compared to the anisotropic CGL regime where energy is diverted into pressure anisotropies.

Shishir Biswas, Masaru Nakanotani, Dinshaw S. Balsara, Vladimir Florinski, Merav Opher2026-03-03🔭 astro-ph

Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition

Neural-POD is a plug-and-play neural operator framework that learns resolution-invariant, nonlinear orthogonal basis functions directly in function space to overcome discretization limitations in AI4Science models, thereby enhancing generalization and interpretability for complex systems like the Burgers' and Navier-Stokes equations.

Changhong Mou, Binghang Lu, Guang Lin2026-03-03🤖 cs.LG

Lattice and Orbital-Resolved Fermiology of Metallenes

This study provides a comprehensive density-functional theory analysis of 45 elemental metallenes across six lattice structures, revealing how lattice geometry and buckling dictate Fermi-surface topology and introducing a predictive "pocketness" score to guide the design and characterization of these materials for applications in plasmonics, catalysis, and quantum optics.

Kameyab Raza Abidi, Mohammad Bagheri, Pekka Koskinen2026-03-03🔬 cond-mat.mes-hall

Imperfect Graphs from Unitary Matrices -- I

This paper introduces a graph-theoretic framework called Topological Structure of Superpositions (TSS), which maps unitary matrices to directed graphs by representing basis states as vertices and non-zero amplitude transitions as edges, thereby isolating the connectivity and reachability properties of quantum operators to provide a novel perspective for analyzing and designing quantum algorithms.

Wesley Lewis, Darsh Pareek, Umesh Kumar, Ravi Janjam2026-03-03🔢 math-ph

mrfmsim: A modular, extendable, and readable simulation package for magnetic resonance force microscopy experiments

This paper introduces mrfmsim, an open-source, modular, and extendable Python package designed to facilitate the accurate simulation, design, and analysis of complex magnetic resonance force microscopy (MRFM) experiments while enhancing reproducibility and development efficiency through its customizable architecture.

Peter Sun, Corinne E. Isaac, Michael C. Boucher, Eric W. Moore, Zhen Wang, John A. Marohn2026-03-03🔬 physics

A comparative study of transformer models and recurrent neural networks for path-dependent composite materials

This study systematically compares Recurrent Neural Networks (RNNs) and Transformer models for predicting the path-dependent behavior of short fiber reinforced composites, revealing that while Transformers offer significantly faster inference, RNNs outperform them in accuracy, particularly on small datasets and during extrapolation.

Petter Uvdal, Mohsen Mirkhalaf2026-03-03🔬 cond-mat.mtrl-sci

Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification

This paper introduces an adaptive uncertainty-guided surrogate framework combining XGBoost and CNNs with self-supervised learning to efficiently model dendritic solidification, significantly reducing the number of costly phase field simulations and associated carbon emissions while maintaining high prediction accuracy.

Eider Garate-Perez, Kerman López de Calle-Etxabe, Oihana Garcia, Borja Calvo, Meritxell Gómez-Omella, Jon Lambarri2026-03-03🤖 cs.AI