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 paper demonstrates the flexibility and modularity of the deal.II library's multigrid infrastructure by solving stationary Stokes, transient Stokes, and incompressible Navier-Stokes equations using various adaptive strategies, including hp-adaptivity, space-time finite elements, and monolithic solvers.
This paper proposes a robust and efficient method to resolve band gap edge eigenstates by decomposing occupation number variance into particle and hole moments, which are then isolated via power narrowing iterations applied to the quasi-purified one-particle density matrix.
This paper demonstrates that using INT8-based emulation via the SCILIB-Accel tool can accelerate FP64 electronic structure calculations on modern GPUs without code modifications, achieving simultaneous improvements in performance and accuracy through tunable precision strategies.
This paper proposes a hard-constrained neural Riemann solver (HCNRS) that enforces five physical constraints—positivity, consistency, mirror symmetry, Galilean invariance, and scaling invariance—to accurately approximate exact Riemann solvers for fluid dynamics while overcoming the conservation errors and symmetry breaking issues found in unconstrained neural approaches.
This paper presents an open-source, third-party-free GPU-accelerated Path Integral Molecular Dynamics (PIMD) code that enables efficient, first-principles simulations of large-scale identical particle systems, successfully modeling up to 40,000 bosons and offering a promising solution to the Fermion sign problem for tens of thousands of fermions.
This paper introduces a first-quantized Matrix Product State (MPS) approach that reformulates fermionic anti-symmetry to achieve entanglement levels comparable to or even lower than those in second quantization, thereby enabling efficient simulation of fermionic many-body systems like the 1D - model.
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 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.