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 paper extends the EPW package to calculate electron-phonon coupling in magnetic materials using the local spin density approximation, revealing that while electron-phonon scattering dominates resistivity in ferromagnetic iron, it plays a minor role in nickel, and confirming that phonon-driven superconductivity is intrinsically suppressed in both systems.
This paper presents a numerical implementation of Jacobi's 1838 analytical solution for geodesics on a triaxial ellipsoid, detailing the accurate evaluation of integrals and the solution of coupled equations to enable the calculation of geodesic paths and distances, as well as the determination of shortest paths between two points.
This paper presents and validates a robust recursive regularised lattice Boltzmann method for incompressible magnetohydrodynamics that utilizes a double-distribution formulation and fourth-order Hermite expansion to enhance numerical stability, reduce lattice-dependent artifacts, and eliminate the need for explicit velocity gradient calculations.
This paper extends an entropic variance-reduction framework to the particle-in-cell method for solving the Vlasov-Poisson equation by introducing a bias-corrected weight distribution via maximum cross-entropy, thereby achieving significant computational speed-ups in low-signal regimes while preserving conservation laws.
This paper introduces a novel code-verification framework for Particle-in-Cell simulations with Direct Simulation Monte Carlo collisions that applies the method of manufactured solutions to particle equations of motion and collision algorithms, enabling the direct computation of discretization and statistical errors without modifying particle weights or distribution functions.
This paper demonstrates that utilizing temperature profiles derived from high-fidelity Multiphysics simulations, rather than uniform profiles, to construct Fission Matrix databases significantly improves the accuracy of multiplication factor and fission source distribution predictions for Molten Salt Fast reactors.
This paper introduces a machine learning-enabled high-throughput screening strategy that identifies 27 high-performing solid acid proton conductors from over six million candidates and reveals a universal 2.5 Å oxygen-oxygen distance as the critical mechanistic factor for proton transfer.
Inspired by the Hohenberg-Kohn theorem, this paper proposes an operator-centered machine learning framework that uses the external nuclear potential as input to construct hierarchical representations for efficiently modeling molecular properties and learning operator-to-operator maps, such as those for Fock and density matrices.
This paper presents a robust, parameter-free truncated-domain framework for electrohydrodynamic simulations of cone-jet flows that leverages inexpensive full-domain electrostatic data to impose accurate boundary conditions, thereby significantly reducing computational costs while maintaining high predictive accuracy without requiring empirical tuning.
This paper introduces a novel particle-based virtual ultrasound machine utilizing a specialized smoothed dissipative particle dynamics variant with implicit pressure solving and negative-pressure stabilization to efficiently simulate acoustic wave-matter interactions across MHz-GHz frequencies, demonstrated through the modeling of microbubble acoustophoresis for drug delivery applications.