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 the effective rank () as a novel quantitative measure for characterizing the expressivity of quantum neural networks and leverages a reinforcement learning framework with a self-attention transformer agent to automatically design highly expressive quantum circuit architectures that maximize this metric.
This article demonstrates that the learnability and scalability of quantum reservoir computing can be continuously optimized by adjusting the proportion of non-Clifford gates, thereby establishing a direct link between reservoir performance, entanglement statistics, and non-stabilizer resources to navigate the boundary between classically simulable and computationally complex quantum dynamics.
This paper introduces the Universal Neural Propagator (UNP), a self-supervised foundation model that learns to predict quantum many-body time evolution across diverse initial states and driving protocols by mapping protocols directly to propagators, thereby enabling transferable simulations beyond the reach of exact diagonalization.
This paper evaluates the performance and portability of various Poisson solvers, including FFT, PCG, FEM, and the novel Particle-in-Fourier (PIF) schemes, within the IPPL library for electrostatic PIC simulations on diverse GPU architectures, finding that while FFT is fastest, the PIF scheme offers excellent scalability as a high-fidelity alternative.
This paper introduces a scalable, translationally invariant variational theory for ab initio polarons that combines momentum-projected wavefunctions with low-rank kernel factorization to accurately model carrier behavior across coupling regimes in the thermodynamic limit, revealing significant biases in existing diagrammatic Monte Carlo results for strong-coupling hole polarons in LiF.
This paper introduces a semi-local framework that integrates polarizable atomic multipoles with non-self-consistent linear response to enable machine learning interatomic potentials to accurately model long-range electrostatics and predict polarization-sensitive observables like Born effective charges and infrared spectra across diverse ionic and polar systems.
This paper presents a data-driven framework using Koopman operator analysis and dynamic mode decomposition to reconstruct band dispersion, spectral functions, and quantum geometric properties directly from spatiotemporal data, offering a unified approach for analyzing wave propagation and topological phases in condensed matter and photonics without requiring an explicit Hamiltonian.
This paper reports an improved Diatomic-In-Molecules (DIM) calculation for excited argon clusters that incorporates previously ignored strongly avoided crossings between 3p4s and 3p4p states to better understand the localization of excitation and the effect of diabatisation on cluster isomers.
This study investigates the strong correlation between classical and quantum chaos in bean- and peanut-shaped billiards by employing a unified analysis of phase-space dynamics, spectral statistics, and dynamical measures, revealing shared chaotic behaviors and eigenfunction scarring in these non-uniform curvature systems.
This study employs multi-dimensional simulations to reveal that nanotextured interfaces in perovskite solar cells enhance power conversion efficiency by redistributing electric fields and modulating carrier dynamics, with specific texturing heights and surface recombination rates at transport layers dictating the resulting open-circuit voltage and short-circuit current density.