1259 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 identifies that measurement-feedback Ising machines suffer from a significantly reduced range of effective hyperparameters compared to their time-continuous analog counterparts due to discrete operation, and proposes an experimentally verified method to mitigate this sensitivity.
This paper introduces a comprehensive database and two efficient, general-purpose machine-learned interatomic potentials (tabGAP and NEP) for nine refractory elements, enabling accurate large-scale simulations of diverse multiphase alloys and complex phenomena like phase transitions and radiation damage.
This paper presents a robust, machine-learned interatomic potential for TiC MXenes that enables efficient molecular dynamics simulations of ion irradiation, providing critical insights into defect statistics and guidelines for defect engineering.
This study utilizes machine learning to reveal that charged domain walls in two-dimensional bismuth possess lower energy than uncharged ones and host topological interfacial states with accidental band crossings at the Fermi level, highlighting their potential for next-generation ferroelectric devices.
This paper introduces a nonparametric framework that optimizes reaction coordinates by incorporating trajectory histories to overcome standard machine learning limitations, enabling robust characterization of rare event dynamics in complex systems like protein folding and climate models without requiring extensive sampling or ground truth data.
This study reveals that Vicsek agents with mutually repelling interactions in a complex noise environment featuring a noiseless circular core exhibit emergent rotational order and a re-entrant global flocking state at high outer noise levels, while demonstrating that particle velocity governs escape dynamics and that gradual noise gradients significantly suppress collective order compared to sharp environmental transitions.
This paper develops a moment-based quasilinear theory to demonstrate that cold electron populations drive secondary instabilities which can nearly completely damp parallel-propagating whistler chorus waves, thereby limiting their amplitude and explaining the rare simultaneous observation of high-amplitude field-aligned and oblique whistler waves in Earth's magnetosphere.
This paper introduces Discrete Solution Operator Learning (DiSOL), a novel paradigm that learns discrete, procedure-based solver stages to accurately and stably solve partial differential equations across varying and topologically complex geometries, addressing the limitations of traditional continuous function-space operator learning in engineering settings.
This paper introduces \textbf{warpax}, a GPU-accelerated Python toolkit that employs continuous gradient-based optimization and automatic differentiation to perform observer-robust energy condition analysis of warp drive spacetimes, revealing that traditional single-frame evaluations significantly underestimate both the spatial extent and magnitude of energy violations.
This paper introduces GET-SEI, a general framework combining graph contrastive learning, extended dynamic mode decomposition, and transition path theory to automatically characterize local atomic environments and quantify lithium transport kinetics and pathways across diverse solid-state electrolyte/lithium metal interfaces for targeted SEI engineering.