1197 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 utilizes a deep neural network trained on density functional theory data to reveal that while increasing pressure up to 1000 MPa enhances the static dielectric constant of liquid water due to higher density and collective dipole fluctuations, it simultaneously reduces the Kirkwood correlation factor by disrupting the ideal tetrahedral hydrogen-bonding network.
This paper introduces an equivariant graph neural network (EGNN) surrogate model that accurately predicts relaxed atomic configurations, formation energies, and strain tensors for lithium cobalt oxide across various compositions, offering a flexible alternative to traditional cluster expansions and reducing the need for computationally expensive density functional theory (DFT) calculations.
This paper introduces a modern Fortran library that calculates SU(3) coupling and recoupling coefficients for specific group chains using established algorithms, demonstrating improved accuracy and a broader range of applicable quantum numbers compared to existing implementations.
This paper reveals that naive context parroting, which simply copies past data, often outperforms sophisticated time-series foundation models in predicting diverse dynamical systems by exposing their tendency to fail through mean convergence, thereby offering a critical baseline and new insights into the mechanisms of in-context learning.
This paper proposes a training-free inverse-design approach that topology-optimizes scattering media in integrated computational spectrometers to decouple hardware design from inference algorithms, achieving superior noise robustness and reconstruction accuracy compared to random scatterers and conventional methods.
This study employs a multiscale framework integrating molecular simulations, process optimization, and techno-economic analysis to evaluate the CALF-20 isoreticular series for biogas upgrading, identifying the parent CALF-20 material as the most economically viable option with a methane production cost of $4.31/kg and energy consumption of 9.35 kWh/kg.
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 paper demonstrates the application of physics-informed neural networks (PINNs) to efficiently compute holographic entanglement entropy and entanglement wedge cross sections for arbitrary subregion shapes within any asymptotically AdS metric, successfully validating the method against known results and showcasing its utility in complex scenarios where traditional calculations are difficult.
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.