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.
This study employs a machine-learned DFT+ approach optimized via Bayesian inference to reproduce quasiparticle self-consistent $GW$ results, identifying CoTiSn and CoZrAl as the most promising half-metallic Heusler candidates for epitaxial growth on InAs while highlighting the critical dependence of spin polarization on the computational method used.
This paper introduces a rigorous, two-step NPT Thermodynamic Integration scheme that eliminates the approximate NVT-to-NPT correction by utilizing an NPT reference with full cell flexibility, thereby providing more accurate and direct Gibbs free energy calculations for crystalline solids, particularly those with complex cell-shape behaviors.
This paper demonstrates that applying Gaussian low-pass filtering to high-dimensional molecular dynamics coordinates effectively mitigates projection artifacts, thereby recovering hidden conformational states and yielding more accurate, long-lived, and structurally defined free energy landscapes.
This paper presents a modular, dependency-isolated Java framework for scientific visualization and simulation that separates 2D and 3D rendering capabilities to support long-lived desktop applications, demonstrated through a real-time gas expansion case study.
This paper presents a novel Arbitrary Lagrangian–Eulerian finite element method for simulating curved and deforming lipid membranes, which decouples mesh motion from lipid flow by introducing a user-specified material-based mesh dynamics constrained by a Lagrange multiplier, while addressing associated numerical instabilities to accurately model biologically significant phenomena like membrane tether pulling.
The paper proposes a Multi-stream Physics Hybrid Network that integrates parallel quantum and classical layers to decompose fluid dynamics solutions into frequency components, achieving significantly lower error rates and higher efficiency than classical models when solving the Navier-Stokes equations for Kovasznay flow.
This paper evaluates an a posteriori turbulence closure for a shell model that integrates physical equations into a neural network, finding that while it successfully reproduces high-order statistical moments, it fails to preserve scale invariance symmetries near the cutoff, revealing a fundamental limitation for subgrid-scale modeling.
This study introduces FlexPINN, an enhanced Physics-Informed Neural Network framework that successfully models fluid dynamics and mass transfer in 3D T-shaped micromixers with various fin geometries and configurations, achieving high accuracy compared to CFD while demonstrating superior mixing efficiency in specific double-unit setups at Reynolds number 40.
This paper presents a comprehensive first-principles atomistic approach using density functional theory to predict the equilibrium morphology of hetero-integrated crystals, which is validated by the strong agreement between its predictions for GaP on Si and experimental Transmission Electron Microscopy observations.
This paper introduces a compact, just-in-time compiled CUDA implementation for Gaussian-type orbital integral kernels that achieves significant speedups (up to 4x) in electron repulsion integral and JK matrix computations compared to state-of-the-art GPU methods, particularly for large basis sets and high angular momentum orbitals.