1255 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 a novel statistical framework that leverages quantum mechanical concepts, specifically density operators and measurement theory, to achieve a symmetry-invariant, data-driven closure for partial differential equations, demonstrating its accuracy in modeling unresolved degrees of freedom for the shallow water equations.
Aitomia is an AI-powered intelligent assistant platform that integrates large language model agents with the MLatom software to democratize and accelerate atomistic and quantum chemical simulations by enabling both experts and non-experts to autonomously set up, run, and analyze complex computational workflows through a user-friendly chat interface.
This paper introduces a novel machine learning framework featuring NAC-specific descriptors and a phase-correction procedure that achieves unprecedented accuracy () in predicting nonadiabatic coupling vectors, enabling robust and efficient fully ML-driven fewest-switches surface hopping simulations for photochemical processes.
This study introduces an analytical coarse-grained potential for anisotropic cellulose nanocrystals, parameterized via Reinforcement Learning to accurately reproduce both structural and mechanical properties across various conditions without requiring additional training.
This paper introduces the rSCANY@rSCANX method, which employs distinct fractions of exact exchange for electronic density and total energy calculations to effectively mitigate self-interaction errors and significantly improve the prediction of electronic, magnetic, and thermochemical properties in transition-metal oxides compared to standard rSCAN and DFT+ approaches.
This paper introduces the SPARSE algorithm, which employs the finite difference method to numerically solve radial Schrödinger equations for inelastic scattering of particles with spin, enabling the calculation of physical K-matrices, scattering poles, and amplitudes.
This paper resolves the Blanc-Cances instability of the Wang-Teter nonlocal kinetic energy density functional in isolated systems by introducing a density-functional-dependent kernel, thereby achieving an order-of-magnitude accuracy improvement for single atoms while maintaining superior performance in bulk metals.
This paper introduces Fermionic-Adapted Shadow Tomography (FAST), a new framework that reformulates dynamical correlation functions to enable their efficient calculation on quantum devices using at most two-copy measurements, thereby significantly reducing the required sample complexity and number of measurement circuits compared to traditional brute-force strategies.
This paper presents a consistent kinetic modeling and discretization strategy using a double-distribution quasi-equilibrium approach that enables accurate, stable, and Galilean-invariant simulations of compressible flows across all Prandtl numbers and specific heat ratios, successfully recovering Navier-Stokes-Fourier physics for both moderate supersonic speeds and complex discontinuities.
This paper presents an efficient topology optimization framework for nonlinear structures that leverages spectral submanifold reduction to analytically compute forced response curves and their sensitivities, enabling the targeted design of MEMS devices with optimized peak amplitudes, hardening/softening behaviors, and bifurcation characteristics.