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 demonstrates that short-range atomic ordering in GeSn semiconductor alloys can be precisely quantified using a machine learning-enabled EXAFS analysis and subsequently tuned via annealing to significantly modify the material's bandgap, establishing local atomic order as a critical new degree of freedom for band engineering alongside composition and strain.
The paper introduces Mat3ra-2D, an open-source framework that enables the rapid, reproducible design of realistic two-dimensional materials and interfaces with defects and disorder to address the limitations of AI/ML models trained solely on ideal bulk crystals.
By combining large-scale lattice Boltzmann simulations with an analytical model, this study reveals that fractal trees in urban environments undergo a drag-crisis transition at high Reynolds numbers that is smoothed by structural complexity and shifted to lower speeds by atmospheric turbulence, challenging the assumption that pruning always reduces aerodynamic loading.
This paper demonstrates that the pseudo-fermion method effectively overcomes the fermion sign problem to accurately simulate the energy of strongly quantum-degenerate uniform electron gases, successfully bridging critical parameter regimes where existing methods like RPIMC and CPIMC fail.
This paper proposes a fast, FFT-based forward and adjoint operator for 3D photoacoustic tomography in planar geometries that exploits transverse shift invariance to replace computationally expensive PDE solvers with depth-dependent 2D convolutions, achieving reconstruction speedups of up to two orders of magnitude.
The paper introduces the FreeGSNKE Pulse Design Tool (FPDT), an open-source Python framework that couples an evolutive equilibrium solver with a virtual control system to simulate, design, and validate tokamak plasma scenarios and control strategies, demonstrating high accuracy against MAST Upgrade experimental data to reduce the need for costly physical testing.
This paper presents a structured reformulation of the many-body dispersion (MBD) model that enables a physically consistent pairwise decomposition of forces, providing a unified framework for energy, force, and Hessian calculations to facilitate interpretable analysis and machine learning surrogate modeling.
This paper introduces a fully automated, gradient-based framework that efficiently extracts interatomic force constants directly from X-ray thermal diffuse scattering data by bypassing traditional computational bottlenecks, thereby enabling high-throughput refinement of lattice dynamics models.
This paper presents a scalable monolithic modified Newton multigrid framework for solving fully implicit space-time discretizations of time-dependent -Navier-Stokes equations, demonstrating robustness and efficiency across shear-thinning regimes and various model parameters through numerical tests.
This paper presents the implementation and evaluation of an asynchronous discontinuous Galerkin method with asynchrony-tolerant fluxes in the deal.II library, demonstrating that this approach recovers high-order accuracy for compressible flow simulations while achieving significant speedups (up to 1.9x) by reducing synchronization overheads in large-scale parallel computing.