1219 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 demonstrates that a machine learning model trained to distinguish the temporal direction of nonequilibrium steady state trajectory segments inherently satisfies a time-local fluctuation theorem, enabling the quantification of thermodynamic reversibility using only local information even for short segments and systems far from equilibrium.
This paper introduces a minimal model of two coupled agents that avoid retracing their paths while attracted to each other's trails, revealing new universality classes characterized by anomalous diffusion, non-Gaussian distributions, and compressed exponential encounter statistics that advance the understanding of memory-driven multi-agent systems.
This paper demonstrates that a computationally efficient workflow combining DFT interface calculations with Koopmans spectral functionals can accurately predict the band structures and level alignments of rutile, anatase, and brookite TiO, offering a promising strategy for screening novel photocatalysts for water splitting.
This paper surveys 35 independent DMRG software packages, highlighting significant feature overlaps and advocating for greater modularity and standardization to reduce duplication, improve interoperability, and enable the tackling of more complex quantum many-body problems.
This paper introduces PHLieNet, a hypernetwork framework that learns a latent embedding of system parameters to dynamically generate weights for a forecasting network, thereby enabling superior generalization and smooth interpolation across diverse parametric regimes in complex dynamical systems compared to existing state-of-the-art methods.
This study employs first-principles calculations to identify and validate Al2Si2N4 and Al2Ge2N4 as stable, efficient two-dimensional octuple-atomic-layer photocatalysts for overall water splitting, demonstrating that nitrogen vacancies further enhance their catalytic activity under acidic and neutral conditions.
This paper presents and validates a novel three-dimensional computational fluid dynamics macro-model that incorporates pore adsorption occupation rate to accurately simulate CO2 adsorption in packed beds, demonstrating that a new high-surface-area geometric design significantly enhances process productivity compared to traditional cylindrical configurations.
The paper introduces GPU-MetaD, a full-life-cycle GPU-accelerated metadynamics framework that integrates machine learning potentials to achieve order-of-magnitude performance gains, enabling ab-initio-level rare-event sampling for million-atom systems and revealing a novel size-dependent two-step nucleation mechanism in gallium nitride.
This paper assesses the lattice Boltzmann method as a time-domain numerical approach for electromagnetic wave scattering by validating its accuracy against analytical and semi-analytical solutions across various canonical benchmarks, including 2D and 3D scattering from dielectric cylinders and spheres with different geometries and material contrasts.
This paper presents a comprehensive set of numerical procedures and algorithms for generating various non-Maxwellian velocity distributions, including , kappa, ring, shell, super-Gaussian, and filled-shell distributions, to be used in particle simulations.