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 presents an end-to-end autonomous LLM agent capable of executing a "mini research loop" on computational physics literature, demonstrating its ability to autonomously identify substantive flaws in 42% of 111 papers and independently generate a publishable critique that revises the conclusions of a specific Nature Communications study.
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 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.
Through numerical hydrodynamic simulations, this study reveals that an active mixture of polar and apolar self-propelled components exhibits a unique regime of spatiotemporal chaos characterized by chaotic band-like structures and proliferating topological defects, driven by a non-monotonic response to the polar component's density and activity.
Flow Gym is a JAX-based framework designed to unify the development, benchmarking, training, and deployment of particle image velocimetry (PIV) and optical-flow methods through a standardized interface that enhances reproducibility and bridges the gap between research and experimental applications.
This paper proposes a computationally efficient, plug-and-play framework for probabilistic constitutive modeling of anisotropic soft tissues that leverages conformalized quantile regression on a thermodynamically consistent, polyconvex formulation to explicitly quantify predictive uncertainty without requiring distributional assumptions.
This paper introduces a model charge density approach to Ewald summations that accelerates convergence by canceling multipole moments of the crystalline charge distribution, a method validated on gallium arsenide and shown to clarify long-standing implementations in the CRYSTAL code.
This paper demonstrates that incorporating quantum-chemical bonding descriptors into machine learning models significantly improves the prediction of elastic, vibrational, and thermodynamic properties of approximately 13,000 solid-state materials while also enabling the discovery of intuitive physical expressions for these properties.
This paper introduces a physics-aware machine learning surrogate for the 3D stochastic Cahn-Hilliard equation that parameterizes inter-cell fluxes to guarantee mass conservation and thermodynamic interpretability, enabling the accurate simulation of noise-driven phenomena like nucleation and coarsening with significant generalization to larger spatial and temporal scales.