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 paper presents a data-driven reduced order model that bypasses iterative wavefunction optimization in Kohn-Sham Density Functional Theory by constructing a low-dimensional basis from representative atomic configurations, enabling efficient and accurate Born-Oppenheimer molecular dynamics simulations as demonstrated on a water molecule.
This study demonstrates that monochromatic irradiation of zigzag monolayer WSe nanoribbons, modeled via a tight-binding Floquet framework and density functional theory, significantly enhances the thermoelectric figure of merit ($ZT > 1$) by reshaping electronic band structures and reducing lattice thermal conductivity through anharmonic scattering.
This paper introduces a framework that leverages large language models to guide symbolic regression, successfully discovering accurate, interpretable, and simplified physical laws for perovskite materials while drastically reducing the search space compared to traditional methods.
This paper introduces HELIOS, an open-source C++ and Python software that utilizes the PMCHWT surface integral equation formulation with RWG basis functions to accurately model light scattering by particles in homogeneous, stratified, and periodic environments.
This paper presents an entropy stability analysis of smoothed dissipative particle dynamics (SDPD) for incompressible flows, revealing that the resulting stability conditions depend on the kernel function type, with Lucy, poly6, and spiky kernels sharing one set of conditions while the spline kernel yields a distinct set.
This paper proposes CoK-PCA, a dimensionality reduction method based on the co-kurtosis tensor that outperforms traditional PCA in accurately capturing stiff chemical dynamics and extreme-valued ignition events in combustion datasets by better identifying critical directions in the thermo-chemical state space.
This paper generalizes the widely used Finite Difference Time Domain (FDTD) method to a meshless setting via the Radial Basis Function generated Finite Difference (RBF-FD) approach and evaluates its performance on a simple test problem.
This paper introduces PARPHOM, a parallel computational code designed to overcome the challenges of calculating phonon properties in large-scale twisted two-dimensional material systems by enabling force constant generation, band structure analysis, and finite-temperature dynamics investigations.
This paper presents a theoretical analysis and numerical validation of the Onsager-Regularized Lattice Boltzmann Method, demonstrating that it achieves higher-order accuracy and mitigates lattice isotropy errors in nonlinear thermohydrodynamic simulations on standard lattices without requiring external correction terms.
This paper introduces the NQS Lanczos method, a systematic approach that combines supervised learning and variational Monte Carlo to optimize neural-network quantum states, effectively addressing underfitting and improving energy accuracy in highly frustrated quantum systems with linearly increasing computational cost.