1217 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.
This paper presents a mathematically consistent reduced-order model coupling Kirchhoff-Love plate bending with Reynolds lubrication theory, implemented via a monolithic interior-penalty finite element scheme in FEniCSx, to simulate and analyze the nonlinear fluid-structure interaction dynamics of wafer-to-wafer bonding.
This study reveals that interlayer interactions in bilayer V2S2O significantly modulate valence band structures and suppress piezomagnetism, while gate-voltage modulation induces asymmetric control over spin-polarized transport in Au/V2S2O/Au devices, offering critical insights for optimizing multilayer altermagnetic spintronics.
This study predicts that the experimentally synthesized material LiVF, which simultaneously hosts altermagnetism and charge-order-induced ferroelectricity, enables ultrafast (15-femtosecond) electrically controlled magnetism through strong magnetoelectric coupling.
This study demonstrates that fine-tuning universal machine-learning interatomic potentials on systematically generated structures enables near-DFT accuracy in predicting mixing energies for 2D high-entropy alloys, overcoming the computational limitations of direct DFT calculations for complex systems like experimentally synthesized (Mo,Ta,Nb,W,V)S.
This paper utilizes validated smoothed particle hydrodynamics simulations to investigate the coefficient of restitution in wet collisions within a moderate-to-high Weber number regime, revealing a scaling law dependent on the Stokes number and dimensionless film thickness that exhibits two distinct power-law regimes.
This paper derives an exact reduction of the screened second-order exchange (SOSEX) energy in the uniform electron gas to a triple integral for a specific one-pole screened interaction model, analyzes its asymptotic behavior to constrain the analytic form of screened-exchange corrections, and provides a diagrammatically justified foundation for constructing beyond-RPA functionals.
This paper reviews numerical methods implemented in the open-source toolkit Axiprop for modeling ultrashort, intense laser pulse propagation in plasmas, demonstrating their application in designing optical plasma waveguides and phase-locked flying-focus schemes for laser plasma electron acceleration.
This paper introduces a systematic protocol combining constraint enforcement, flexible functional forms, and modern optimization to develop the COACH functional, a range-separated hybrid meta-GGA that achieves superior accuracy and transferability across molecular benchmarks while highlighting the need for nonlocal information to overcome current performance limits.
This paper demonstrates that a machine learning model trained to distinguish the temporal direction of nonequilibrium steady state trajectory segments inherently satisfies a time-local fluctuation theorem, enabling the quantification of thermodynamic reversibility using only local information even for short segments and systems far from equilibrium.
This paper introduces a minimal model of two coupled agents that avoid retracing their paths while attracted to each other's trails, revealing new universality classes characterized by anomalous diffusion, non-Gaussian distributions, and compressed exponential encounter statistics that advance the understanding of memory-driven multi-agent systems.