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 study validates the accuracy of the UMA universal machine learning interatomic potential for 25 different MoS2 dopants and demonstrates its ability to efficiently simulate complex phenomena like dopant clustering and layer fracturing, thereby enabling high-throughput computational design of doped MoS2 for targeted applications.
The paper introduces MDIntrinsicDimension, an open-source Python package that estimates the intrinsic dimension of molecular dynamics trajectories using invariant projections and state-of-the-art estimators to provide detailed, time-resolved insights into the flexibility and heterogeneity of macromolecular motions.
This paper proposes a corrected open boundary framework based on the Multiple-relaxation-time (MRT) lattice Boltzmann method for immiscible pseudopotential models, which significantly reduces spurious currents and ensures global mass conservation through improved inlet reconstruction, dynamic outlet velocity adjustment, and viscosity-based stability tuning, as validated by four benchmark simulations showing high accuracy in droplet morphology and flow behavior.
This paper introduces *sangkuriang*, an open-source Python library that utilizes Fourier pseudo-spectral methods and JIT-accelerated Runge-Kutta integration to accurately simulate Korteweg-de Vries soliton dynamics, demonstrating high-fidelity conservation laws and phase-space preservation across various interaction scenarios.
This paper presents a topology optimization framework based on time-dependent Ginzburg-Landau theory under the Weyl gauge to inversely design the structural geometries of type-II superconductors with superconductor-dielectric/vacuum interfaces, aiming to enhance flux pinning and current density through optimal defect placement.
This paper proposes a computationally efficient enhanced sampling method that approximates the machine-learned committor function entirely within descriptor space to bypass costly coordinate gradients, thereby retaining robust sampling performance while enabling the study of rare events previously infeasible with the original formulation.
This paper presents an open-source implementation and reproducible benchmarks using libROM, Laghos, and MFEM to evaluate the trade-offs between accuracy and computational efficiency of various hyper-reduction methods, such as gappy POD interpolation and the empirical quadrature procedure, across nonlinear diffusion, elasticity, and Lagrangian hydrodynamics problems, ultimately demonstrating that optimal method selection depends on the specific physics and time integration scheme employed.
This paper introduces the Random Batch Sum-of-Gaussians (RBSOG) method, a scalable and efficient algorithm for NPT molecular dynamics simulations that utilizes a novel pressure kernel splitting and measure-recalibration strategy to achieve significant speedups and variance reduction while accurately reproducing structural and dynamical properties of large charged systems.
This paper presents a robust machine learning framework that combines parameter estimation, cosine transform inversion, and constrained neural networks to accurately reconstruct spectral density functions in open quantum systems from noisy time-domain measurements.
This workshop report outlines a comprehensive strategy for advancing sustainability and ethics in ErUM-Data research by integrating technical measures like CO2 reduction and AI governance with cultural shifts in education, funding, and community engagement to embed responsible practices into everyday scientific workflows.