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.

A distribution-free lattice Boltzmann method for compartmental reaction-diffusion systems with application to epidemic modelling

This paper introduces a distribution-free, single-step simplified lattice Boltzmann method (SSLBM) for compartmental reaction-diffusion systems that eliminates particle distribution functions to achieve a compact, efficient framework, demonstrating superior accuracy and robustness over standard lattice Boltzmann and finite difference approaches when applied to SEIRD epidemic modeling.

Alessandro De Rosis2026-03-23🔬 physics

First-principle study of the influence of hydroxyapatite on magnesium surfaces

This study utilizes density functional theory to demonstrate that calcium and zinc doping of magnesium surfaces enhances hydroxyapatite adsorption and induces significant structural and electronic changes, including dopant migration and surface deformation, thereby influencing the performance of Mg-based biodegradable implants.

Anthony Veit Berg, Ablai Forster, Tim Hansson, Alexandra J. Jernstedt, Emmy Salminen, Elsebeth Schröder2026-03-23🔬 cond-mat.mtrl-sci

Physics-informed Bayesian Optimization for Quantitative High-Resolution Transmission Electron Microscopy

This paper introduces a physics-informed Bayesian optimization framework that automates and significantly accelerates the quantification of atomic structures in high-resolution transmission electron microscopy, achieving a three-to-four order-of-magnitude improvement in time efficiency for determining 3D crystal structures from single images.

Xiankang Tang, Yixuan Zhang, Juri Barthel, Chun-Lin Jia, Rafal E. Dunin-Borkowski, Hongbin Zhang, Lei Jin2026-03-23🔬 physics

Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians

This paper introduces HamGNN-LR, a machine-learning framework that integrates physics-informed long-range Coulomb corrections via variational decomposition and Ewald summation to overcome the limitations of short-range models in accurately predicting electronic properties of polar crystals and heterostructures.

Yang Zhong, Xiwen Li, Xingao Gong, Hongjun Xiang2026-03-23🔬 cond-mat.mtrl-sci

Time-delay estimation using the Wigner-Ville distribution

This paper proposes a time-delay estimation method based on the Wigner-Ville Distribution (WVD) that outperforms traditional linear time-frequency approaches like the continuous wavelet transform by achieving higher accuracy and lower uncertainty, particularly for nonstationary signals in energetic frequency bands.

L. de A. Gurgel, J. M. de Araújo, L. D. Machado, P. D. S. de Lima2026-03-23🔬 physics

Micromagnetic Modeling of Surface Acoustic Wave Driven Dynamics: Interplay of Strain, Magnetorotation, and Magnetic Anisotropy

This paper presents a unified micromagnetic study of surface acoustic wave-driven spin wave dynamics in CoFeB films, demonstrating that the orientation of magnetic anisotropy acts as a tunable parameter to optimize resonant coupling, particularly when the wave propagates parallel to the external magnetic field.

Florian Millo, Pauline Rovillain, Massimiliano Marangolo, Daniel Stoeffler2026-03-23🔬 cond-mat.mes-hall

Deep learning-based phase-field modelling of brittle fracture in anisotropic media

This paper introduces a novel variational physics-informed deep learning framework that utilizes higher-order B-spline basis functions within the Deep Ritz Method to accurately model brittle crack propagation in anisotropic media, overcoming previous limitations of isotropic and second-order formulations.

N. Plung\.e, P. Brommer, R. S. Edwards, E. G. Kakouris2026-03-23🔬 physics

A log-linear time algorithm for the elastodynamic boundary integral equation method

This paper introduces a fast and memory-efficient algorithm called FDP=H-matrices that utilizes fast domain partitioning and plane-wave approximations to solve transient elastodynamic boundary integral equations with $\mathcal{O}(N \log N)$ memory and $\mathcal{O}(NM \log N)$ computation time, significantly reducing the costs of traditional time-marching implementations while maintaining accuracy.

Dye SK Sato, Ryosuke Ando2026-03-20🔬 physics

Reaching precise proton affinities in non-Born-Oppenheimer calculations

This study demonstrates that non-Born-Oppenheimer proton affinity calculations achieve high accuracy (within 0.1 kcal/mol) primarily by using uncontracted electronic basis sets on quantum protons, which significantly improves convergence while rendering the choice of protonic basis sets less critical.

Luukas Nikkanen, Susi Lehtola2026-03-20🔬 physics

From Polyhedra to Crystals: A Graph-Theoretic Framework for Crystal Structure Generation

This paper introduces a novel graph-theoretic framework that encodes the geometry and topology of space-filling polyhedra into dual periodic graphs to systematically generate crystal structures, offering a more efficient and interpretable alternative to conventional random generation methods for accelerating materials discovery.

Tomoyasu Yokoyama, Kazuhide Ichikawa, Hisashi Naito2026-03-20🔬 cond-mat.mtrl-sci

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