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.

Magnetism Induced by Azanide and Ammonia Adsorption in Defective Molybdenum Disulfide and Diselenide: A First-Principles Study

This first-principles study reveals that while pristine chalcogen vacancies in MoS $_2$ and MoSe $_2$ do not induce magnetism, the adsorption of azanide (NH $_2$ ) and ammonia (NH $_3$ ) on these defective monolayers generates localized magnetic moments, with MoSe $_2$ exhibiting a notable 2.0 $\mu_B$ moment upon NH $_3$ dissociation, thereby demonstrating a viable strategy for tuning magnetism in 2D materials for spintronic applications.

Guilherme S. L. Fabris, Bruno Ipaves, Raphael B. Oliveira, Humberto R. Gutierrez, Marcelo L. Pereira Junior, Douglas S. Galvão2026-01-30🔬 cond-mat.mtrl-sci

Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening

This paper presents a generative model-driven framework combined with hierarchical thermodynamic and electronic screening to successfully identify 13 new thermodynamically stable electrides and 264 electron-rich compounds from thousands of chemical compositions, accelerating the discovery of materials with exceptional electronic properties.

Shuo Tao, Qiang Zhu2026-01-30🔬 cond-mat.mtrl-sci

A Hybrid semi-Lagrangian Flow Mapping Approach for Vlasov Systems: Combining Iterative and Compositional Flow Maps

This paper proposes a hybrid semi-Lagrangian scheme for the Vlasov-Poisson equation that synergistically combines the Numerical Flow Iteration (NuFI) method's conservative local time-stepping with the Characteristic Mapping Method's (CMM) efficient global submap composition to achieve a balance between computational cost, storage requirements, and structural preservation.

Philipp Krah, Zetao Lin, R. -Paul Wilhelm, Fabio Bacchini, Jean-Christophe Nave, Virginie Grandgirard, Kai Schneider2026-01-30🔢 math

MEIDNet: Multimodal generative AI framework for inverse materials design

This paper introduces MEIDNet, a multimodal generative AI framework that combines equivariant graph neural networks and contrastive learning to efficiently accelerate the inverse design of novel, stable materials with targeted properties, as demonstrated by the successful generation of low-bandgap perovskites.

Anand Babu, Rogério Almeida Gouvêa, Pierre Vandergheynst, Gian-Marco Rignanese2026-01-30🔬 cond-mat.mtrl-sci

Loss Landscape Geometry and the Learning of Symmetries: Or, What Influence Functions Reveal About Robust Generalization

This paper introduces an influence-based diagnostic that analyzes the local geometry of the loss landscape to determine whether neural emulators of partial differential equations have successfully internalized physical symmetries by measuring the coherence of gradient updates along symmetry-related orbits, thereby offering a novel method to evaluate robust generalization beyond standard forward-pass tests.

James Amarel, Robyn Miller, Nicolas Hengartner, Benjamin Migliori, Emily Casleton, Alexei Skurikhin, Earl Lawrence, Gerd J. Kunde2026-01-29🤖 cs.LG

Quantum statistics from classical simulations via generative Gibbs sampling

The paper introduces GG-PI, a computationally efficient framework that leverages generative modeling and Gibbs sampling on classical simulation data to accurately recover nuclear quantum effects and transfer across temperatures without retraining, significantly outperforming traditional path integral molecular dynamics.

Weizhou Wang, Xuanxi Zhang, Jonathan Weare, Aaron R. Dinner2026-01-29🔬 cond-mat

Numerically Consistent Non-Boussinesq Subgrid-scale Stress Model with Enhanced Convergence

This paper presents a numerically consistent, non-Boussinesq subgrid-scale stress model for large-eddy simulation that leverages data assimilation and multi-task learning to achieve enhanced convergence and improved predictions of turbulent boundary layers under adverse pressure gradients compared to the Dynamic Smagorinsky model.

Yuenong Ling, Adrián Lozano-Durán2026-01-29🔬 physics

An Empirical Investigation of Neural ODEs and Symbolic Regression for Dynamical Systems

This paper demonstrates that combining Neural Ordinary Differential Equations (NODEs) for effective data extrapolation with Symbolic Regression (SR) for equation recovery offers a promising hybrid approach to discovering governing physical laws from limited and noisy data.

Panayiotis Ioannou, Pietro Liò, Pietro Cicuta2026-01-29🤖 cs.LG

High-order exponential solver method for particle-in-cell simulations

This paper introduces a finite difference exponential time domain solver for particle-in-cell simulations that bridges the gap between standard finite-difference and spectral methods, offering high accuracy and improved locality in 3D while demonstrating its effectiveness through various laser-plasma interaction benchmarks.

Szilárd Majorosi, Nasr Hafz, Zsolt Lécz2026-01-28🔬 physics

A finite element solver for a thermodynamically consistent electrolyte model

This paper presents a thermodynamically consistent, finite element-based electrolyte solver implemented in FEniCSx that accurately models multicomponent ionic transport by incorporating steric effects, solvation, and pressure coupling, thereby improving physical fidelity and numerical stability over classical frameworks for high-concentration electrochemical systems.

Jan Habscheid, Satyvir Singh, Lambert Theisen, Stefanie Braun, Manuel Torrilhon2026-01-28💻 cs

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