physics.comp-ph papers | Gist.Science

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.

Comprehensive full-f drift-kinetic and delta-f gyrokinetic simulations of a linear plasma device based on the gyro-moment approach

This paper presents the first comprehensive full-f drift-kinetic and $\delta$ -f gyrokinetic simulations of the LAPD linear plasma device, revealing that ion distributions are approximately bi-Maxwellian and that Kelvin-Helmholtz-driven turbulence dominates the system, with small-scale structures only significantly amplified under reduced collisionality and enhanced source conditions.

Jacob Emil Mencke, Paolo Ricci2026-03-16🔬 physics

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Research

The paper introduces QMatSuite, an open-source platform that enables AI agents in computational materials science to consolidate experimental knowledge through provenance tracking and reflection, significantly reducing reasoning overhead and improving accuracy across both familiar and unfamiliar material simulations.

Haonan Huang2026-03-16🔬 cond-mat.mtrl-sci

Rigorous foundations of adaptive mode tracking in single-parametric Hermitian eigenvalue problems: existence theorems, error indicators, and application to SAFE dispersion analysis

This paper establishes a rigorous theoretical framework and proposes an adaptive sampling algorithm for robust mode tracking in single-parametric Hermitian eigenvalue problems, specifically addressing challenges in SAFE dispersion analysis such as mode veering and degeneracy through novel error indicators and symmetry-aware subspace metrics.

Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi2026-03-16🔬 physics

Initial tensor construction and dependence of the tensor renormalization group on initial tensors

This paper proposes a singular-value-decomposition-free method for constructing initial tensor networks and demonstrates that while Tensor Renormalization Group (TRG) results depend heavily on these initial tensors, this dependence can be eliminated and numerical robustness significantly improved by employing the boundary TRG technique.

Katsumasa Nakayama, Manuel Schneider2026-03-13⚛️ hep-lat

Parton Distribution Functions in the Schwinger model from Tensor Network States

This paper proposes and demonstrates a method using tensor network states within the Hamiltonian formalism to accurately compute parton distribution functions for the vector meson in the massive Schwinger model directly in Minkowski space, thereby overcoming the limitations of Euclidean lattice calculations and offering a pathway for quantum simulations.

Mari Carmen Bañuls, Krzysztof Cichy, C. -J. David Lin, Manuel Schneider2026-03-13⚛️ hep-lat

Hole-doping reduces the coercive field in ferroelectric hafnia

This study predicts that hole doping reduces the coercive field in ferroelectric hafnia from 8 MV/cm to 6 MV/cm by activating a lower-energy polarization switching pathway through the Pbcm phase, thereby transforming the material from an improper to a proper ferroelectric.

Pravan Omprakash, Gwan Yeong Jung, Guodong Ren, Rohan Mishra2026-03-13🔬 cond-mat.mtrl-sci

Short-Range Order and Li $_x$ TM $_{4-x}$ Probability Maps for Disordered Rocksalt Cathodes

This study employs exhaustive Monte Carlo mapping to reveal that short-range order parameters, rather than simple low-temperature long-range order attenuation, govern Li $_4$ tetrahedron probabilities in disordered rocksalt cathodes, thereby proposing strategies to surpass the random limit and enhance these critical clusters for improved battery performance.

Tzu-chen Liu, Steven B. Torrisi, Chris Wolverton2026-03-13🔬 cond-mat.mtrl-sci

CuPyMag: GPU-Accelerated Finite-Element Micromagnetics with Magnetostriction

CuPyMag is an open-source, GPU-accelerated Python framework that leverages finite elements and magnetoelastic coupling to achieve up to two orders of magnitude speedup over CPU codes, enabling efficient, large-scale micromagnetic simulations of realistic defect geometries and nanoscale structures.

Hongyi Guan, Ananya Renuka Balakrishna2026-03-13🔬 physics

The Spin-MInt Algorithm: an Accurate and Symplectic Propagator for the Spin-Mapping Representation of Nonadiabatic Dynamics

This paper introduces the Spin-MInt algorithm, the first rigorously symplectic and time-reversible propagator designed to directly simulate nonadiabatic dynamics using spin-mapping variables, demonstrating superior accuracy and computational efficiency compared to existing methods across various models.

Lauren E. Cook, James R. Rampton, Timothy J. H. Hele2026-03-13🔬 physics

A hybrid Green-Kubo (hGK) framework for calculating viscosity from short MD simulations

This paper introduces a hybrid Green-Kubo (hGK) framework that significantly reduces the computational cost of calculating viscosity in molecular dynamics simulations by combining short-time ballistic data from brief trajectories with analytically fitted long-time relaxation tails, thereby enabling accurate predictions for complex systems like polymers and electrolytes where traditional methods fail to converge.

Akash K. Meel, Santosh Mogurampelly2026-03-13🔬 physics

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