903 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 introduces version 2.0 of SmoQyDQMC.jl, a flexible Julia package that implements the determinant quantum Monte Carlo algorithm to simulate Hubbard and generalized electron-phonon interactions using an optimized hybrid Monte Carlo method with exact forces for efficient phonon sampling.
This molecular dynamics study demonstrates that the creep resistance of MoNbTaW refractory high-entropy alloys is significantly enhanced by increasing grain size and introducing local chemical order, as both factors strengthen grain boundaries and suppress grain-boundary-dominated deformation mechanisms.
This paper elucidates the mechanism behind the high transition temperature in sulfur superhydride HS by demonstrating that its valence states are plane-wave-like, leading to a density-of-states peak near the Fermi level through the hybridization of specific plane waves driven by the adjacency of Jones' large zone to the Fermi surface.
This paper demonstrates that the previously developed imaginary-time multi-electronic-state path integral (MES-PI) formulation naturally captures the geometric phase effect arising from conical intersections, and quantifies its impact on low-temperature thermodynamic properties using an ad hoc GP-excluded construction as a comparison baseline.
This paper demonstrates that conventional machine learning interatomic potentials fail to accurately model mixed-valence materials like NaFePO4 due to their inability to capture electronic entropy, but introducing explicit charge-state information into the potential's representation successfully resolves these errors and enables correct structural optimization and thermodynamic predictions.
This paper investigates the vacuum states and qualitative patterns of a non-local q-color Potts model on a random two-dimensional lattice through numerical simulations, exploring its conjectured relationship to the chromatic number of a plane problem.
The paper introduces pylevin, a Python package that utilizes Levin's method to efficiently and accurately compute highly oscillatory integrals containing up to three Bessel functions, offering performance comparable to specialized single-function tools and significantly outperforming standard quadrature methods for multi-function integrals.
This paper introduces a new fixed-point iteration scheme based on the Bidirectional Pulse Propagation Equations to solve nonlinear electromagnetic scattering problems in slab-like geometries with arbitrary material responses, demonstrating its convergence and accuracy through a case study involving mixed fast electronic and slow molecular vibrational responses.
This paper introduces "split-flows," a novel flow-based framework that treats backmapping as a continuous-time measure transport to enable expressive conditional sampling of atomistic structures and, for the first time, provide a tractable method for computing mapping entropies to quantify information loss in coarse-grained molecular models.
This paper introduces rigorous metrics to analyze how unconstrained machine learning models learn physical symmetries, demonstrating that strategically injecting minimal inductive biases can achieve superior stability and accuracy while preserving the scalability of unconstrained architectures.