1164 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 a hierarchical generative optimization framework that couples genetic algorithms with generative models to enable the automated, data-driven design of complex multi-component systems, successfully demonstrating a 30% reduction in activation barriers for catalytic environments through joint optimization of molecular composition and spatial arrangement.
This paper introduces X-MACE, a transferable machine-learning potential combined with curvature-driven surface hopping, to efficiently screen fluorescent protein chromophores and reveal how steric crowding and conjugation extension govern their excited-state dynamics and fluorescence properties.
This paper demonstrates that a network of Kerr parametric oscillators functions as an "Ising selector machine" capable of targeting specific ground, excited, or highest-energy states of an Ising Hamiltonian by tuning the pump-cavity frequency detuning, thereby enabling applications in Boltzmann sampling and spectral analysis.
This paper demonstrates that combining infrared valley excitation with a THz pulse envelope enables the all-optical generation of nearly 100% pure spin currents in the 2D altermagnet CrSO, overcoming previous symmetry-based limitations to establish a practical route for lightwave spin control in low spin-orbit coupling materials.
This paper demonstrates that the radiative and nonradiative decay rates of a fluorescent molecule can be continuously and reversibly tuned by up to two orders of magnitude through electrical shifting of a Fano resonance-induced transparency, offering significant potential for integrated quantum technologies and advanced imaging applications.
This study systematically investigates how compositional variations and bilayer thicknesses influence the mechanical properties, reaction dynamics, and phase evolution of Ni/Al reactive multilayers, revealing that composition enables precise tuning of reaction characteristics while kinetic factors drive non-equilibrium phase formation, all validated through an integrated approach of experimental testing and molecular dynamics simulations.
This paper proposes an efficient, deterministic algorithm that accelerates the inchworm method for simulating open quantum systems by approximating the bath influence functional as a tensor train, thereby achieving linear scaling with dimensionality and enabling accurate long-time dynamics simulations.
This paper introduces a gradient-optimized neuroevolution potential (GNEP) framework that leverages analytical gradients and the Adam optimizer to achieve orders-of-magnitude faster training and rapid convergence while maintaining high accuracy and transferability for large-scale molecular dynamics simulations of Sb-Te materials.
Using ab initio real-time time-dependent density functional theory, this study demonstrates that above a critical current threshold, nonequilibrium charge flow in chiral wires dynamically lifts time-reversal symmetry constraints to induce pronounced spin and orbital polarizations, revealing an intrinsic mechanism for chirality-induced spin selectivity.
This study demonstrates that combining Minkowski Functionals with Conditional Moments of Derivatives (CMD) via simulation-based inference yields significantly tighter cosmological constraints on and than either statistic alone, with the CMD-enhanced morphological approach outperforming the redshift-space halo power spectrum in mass-selected halo configurations by capturing complementary anisotropic and nonlinear features.