917 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 and validates a generalized algorithm for efficient Monte-Carlo sampling of metastable systems using non-local updates in collective-variable space under underdamped Langevin dynamics, demonstrating substantial performance improvements over previous overdamped approaches and extending the applicability of machine-learning-based samplers to more realistic molecular systems.
This paper demonstrates that the Transient Time Correlation Function (TTCF) method is a computationally efficient and precise alternative to traditional time-averaging approaches for calculating nonequilibrium transport coefficients in both linear and nonlinear regimes, as validated through case studies of the Lorentz gas and anharmonic oscillator chains.
The paper introduces **unxt**, a Python package built on the **quax** framework that integrates **astropy.units** with **JAX** to enable seamless, high-performance, unit-aware scientific computing.
This paper presents a comprehensive analysis and the first open-source implementation of phase-shifting dealiasing methods for pseudo-spectral simulations of incompressible Navier-Stokes equations, demonstrating that these techniques can achieve up to a threefold speedup over standard 2/3 truncation with minimal accuracy loss.
This paper provides a rigorous theoretical justification for the noise-cancellation algorithm in interacting Brownian systems by proving that cross-correlations vanish in thermal equilibrium—rendering the method exact—while demonstrating that finite cross-correlations in nonequilibrium systems serve as a distinct fingerprint of non-equilibrium physics requiring specific corrections.
This paper introduces a robust, high-order numerical framework for simulating next-generation solar cells that combines structure-preserving spatial discretization with L-stable implicit Runge-Kutta time integration to accurately model coupled charge, exciton, and ion transport dynamics without empirical parameters.
This paper proposes a two-dimensional multi-phase-field model based on Puck failure theory and a mesh overlay method to accurately predict and simulate various in-plane damage modes in fiber-reinforced composite laminates, demonstrating strong agreement with experimental results across multiple benchmark loading scenarios.
This paper demonstrates that combining highly accurate machine-learned potentials with the quantum thermal bath method provides a computationally efficient and reliable approach for simulating the infrared spectra of protonated water clusters, offering a cost-effective alternative to traditional quantum dynamics techniques.
This paper numerically investigates how external magnetic fields influence the long-range transport of large polarons (solitons) in discrete quasi-one-dimensional systems, demonstrating that the effect depends on both field strength and specific system parameters that define soliton properties, while also examining polarons generated by donor complexes.
This paper demonstrates that the fully relativistic fixed spin moment (FR-FSM) method reconciles discrepancies in magnetocrystalline anisotropy energy (MAE) calculations arising from different exchange-correlation potentials and provides a framework for estimating maximum MAE values to guide the design of new-generation permanent magnets.