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 study introduces the Northeast Materials Database (NEMAD), a comprehensive resource of 67,573 magnetic materials entries generated via Large Language Models, and demonstrates its utility in training machine learning models that achieve high accuracy in classifying magnetic types and predicting transition temperatures to accelerate the discovery of high-performance magnetic materials.
This paper introduces a pseudo-fermion propagator approach that resolves the fermion sign problem in path integral Monte Carlo simulations by utilizing the absolute value of the fermionic determinant and an energy shift, enabling accurate first-principles calculations of fermionic systems across various degeneracy regimes.
This molecular dynamics study demonstrates that ultrasound-assisted cold spray significantly enhances the plastic deformation and interfacial bonding of tungsten through acoustic softening and transient thermal activation, offering a viable solution for the additive manufacturing of refractory metals and heterogeneous alloys.
This paper presents and validates a novel two-level fluid-kinetic coupling method that combines Direct Simulation Monte Carlo (DSMC) for near-wall layers with a high-order Lattice-Boltzmann (HOLB) scheme for bulk flow, enabling the first computationally feasible simulation of coherent structure regeneration cycles and transition to turbulence in wall-bounded flows at Reynolds numbers up to thousands.
The paper introduces SCALE-TRACK, an open-source, asynchronous Euler-Lagrange particle tracking algorithm designed for heterogeneous exascale architectures that achieves unprecedented scalability by successfully simulating up to 256 billion particles across 256 GPUs while maintaining accuracy and efficiency.
This paper introduces a novel method using cross-scale graph-pooled Chebyshev signatures to automatically characterize, classify, and cluster complex atomistic structural transitions in large-scale simulations by defining a physically meaningful distance metric that outperforms traditional analysis techniques.
This paper presents a neural-operator-accelerated concurrent multiscale framework that couples atomistic simulations with continuum finite-element analysis using a Recurrent Neural Operator surrogate to efficiently and accurately model the history-dependent viscoelastic behavior of materials like polyurea at scales previously untractable for direct molecular dynamics coupling.
The paper introduces PRBench, a rigorous benchmark comprising 30 expert-curated physics tasks for evaluating the end-to-end reproduction capabilities of AI agents, revealing that current models struggle significantly with code correctness, data accuracy, and achieving successful reproduction despite their advanced reasoning abilities.
This paper presents an exact analytical phase-space solution for the power-law damped contact oscillator by transforming its nonlinear dynamics into an equivalent linear spring-dashpot system, thereby deriving closed-form expressions for impact characteristics and proving that the coefficient of restitution is independent of initial velocity for all force-law exponents.
This paper presents a data-driven approach that constructs a generalized, anisotropic collision operator for 1D-3V inhomogeneous plasmas by learning directly from molecular dynamics simulations, thereby bridging micro-scale interactions and macroscopic kinetic descriptions while ensuring strict conservation laws and efficient numerical evaluation.