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 resolves the long-standing structural mystery of the metastable silicon phase Si-XIII by integrating advanced theoretical modeling with experimental characterization to propose and validate a crystal structure that consistently explains all observed physical and chemical signatures.
This paper presents a new molecular dynamics simulation using the Fast Multipole Method to efficiently model laser cooling in large 3D Penning trap ion crystals, demonstrating that thousands of ions can be cooled to ultracold temperatures with linear computational scaling, thereby validating their potential for future quantum science experiments.
The paper introduces SODAs, a data-driven sparse optimization method that sequentially discovers the dynamic and algebraic components of differential-algebraic equations in their explicit form without prior variable elimination, thereby enabling the identification of interpretable, structure-preserving models for complex systems with unknown constraints.
This paper proposes an adaptive, physics-inspired framework for designing machine-learning interatomic potentials that utilizes iterative model reconfiguration guided by Fisher information matrix analysis and multi-property error metrics to achieve optimal performance with minimal parameters.
This paper proposes a novel approach to defining metastable states by optimizing the shape of local domains to maximize a timescale separation metric, utilizing derived analytic expressions for Dirichlet eigenvalue variations and high-dimensional tractability methods to significantly improve upon conventional definitions in molecular simulations.
This study establishes a robust framework for modeling electrified metal-electrolyte interfaces by systematically deriving effective electrode-ion interactions from free-energy profiles, demonstrating that precise force field parameterization and machine-learned potentials are critical for accurately capturing specific ion adsorption effects that significantly alter interfacial properties like the potential of zero charge and differential capacitance.
This paper introduces a fast and flexible probabilistic forecasting framework for dynamical systems that combines flow matching to generate physically consistent initial perturbations with deterministic ODE-based propagation, achieving state-of-the-art accuracy and efficiency compared to diffusion-based baselines.
This paper proposes two explicit, meshless computational electromagnetics methods based on local radial basis function interpolation that, when augmented with hyperviscosity terms, achieve convergence and offer improved numerical dispersion and reduced anisotropy compared to the traditional finite difference time domain method.
This paper presents an eigenvalue-accelerated optimization method that utilizes a fast shift-invert eigensolver to eliminate ill-conditioning and achieve orders-of-magnitude speedup in designing high- optical resonant cavities for maximizing the local density of states.
MaxwellLink is a modular, open-source Python framework that enables massively parallel, self-consistent simulations of light-matter interactions by seamlessly coupling diverse electromagnetic solvers with various molecular dynamics drivers via a socket-based interface, thereby overcoming traditional scale limitations to explore complex phenomena in spectroscopy, quantum optics, and polaritonics.