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 Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampling method that accelerates molecular dynamics simulations by 3–9x using a fast draft model and parallel verification without introducing relative error or compromising the accuracy of the target model's distribution.
This paper presents a diagrammatic Monte Carlo method that stochastically samples and resums ladder series contributions to the positron self-energy in molecules, achieving significant memory reduction compared to deterministic Bethe-Salpeter equation solutions while demonstrating quantitative agreement with exact diagonalization benchmarks for lithium hydride.
This paper introduces a novel data-driven reduced-order modeling framework that utilizes Henon neural networks to construct an end-to-end symplectic architecture, ensuring exact preservation of the symplectic structure and long-term stability for high-dimensional Hamiltonian systems.
This paper presents a validated implementation of the GW-BSE formalism in the CP2K code to accurately predict the optical spectra and excitation sizes of nanographenes, demonstrating its superiority over time-dependent density functional theory for describing electronic excitations in nanostructures.
The paper introduces ELECTRAFI, a fast and differentiable model that predicts periodic charge densities in crystalline materials by leveraging closed-form Fourier transforms of anisotropic Gaussians to achieve state-of-the-art accuracy with up to 633 times faster inference, thereby significantly reducing the total computational cost of DFT calculations.
This paper proposes a permutation-invariant autoencoder framework that learns low-dimensional latent representations and macroscopic dynamics for unordered microscopic systems by reconstructing mass distributions rather than fixed-order samples, demonstrating robust performance across particle systems, fluids, and polymer video data.
This paper introduces a wetting boundary condition for curved solid surfaces in the color-gradient lattice Boltzmann method by updating order parameters on ghost nodes, a scheme validated on GPU hardware to effectively handle large density and viscosity contrasts while minimizing spurious currents and accurately reproducing both static and dynamic contact line behaviors.
This paper introduces "spectral position" to demonstrate that larger neural models achieve superior performance by extending their learning capacity into the spectral tail of the empirical neural tangent kernel, a capability enabled by feature learning that adaptively amplifies gradients to access weak signals inaccessible to smaller models.
Drawing from a May 2023 workshop, this paper calls for resource-aware research in the fields of Universe and Matter by outlining a portfolio of digital transformation measures designed to simultaneously advance scientific progress and mitigate climate change through reduced fossil fuel reliance.
This study employs machine-learning force fields to conduct large-scale molecular dynamics simulations, revealing that optimal Li-ion transport and channel stability in amorphous Ti-doped lithium phosphorus sulfide electrolytes occur at 10% and 20% Ti concentrations via free-volume diffusion facilitated by disordered Li-S polyhedra.