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 machine-learning-accelerated computational approach that uses surface-specific vibrational spectroscopy to demonstrate how graphene oxidation alters interfacial water structure, providing a quantitative spectroscopic fingerprint to reconcile conflicting experimental data.
This study proposes an active learning approach to improve the prediction of enthalpy of mixing in binary liquids by identifying data gaps and performing targeted *ab initio* molecular dynamics simulations, specifically focusing on refractory elements.
This paper proposes a method for constructing vector potentials for harmonic fields tangent to a boundary in domains with tunnels by solving curl-curl problems with inhomogeneous boundary conditions on curves that represent a basis for the domain's 1-chain homology group.
The paper introduces a data-informed quantum-classical dynamics (DIQCD) approach that uses a flexible, time-dependent Lindblad equation to accurately predict the evolution of open quantum systems by optimizing a Hamiltonian to fit sparse and noisy experimental or simulated data.
This paper introduces high-order, entropy-stable discontinuous spectral-element methods for the rotating shallow water equations on curved manifolds that achieve mass and energy conservation, well-balancing for variable topography, and exact geometric representation through a skew-symmetric covariant formulation.
This paper explores the application of quantum-inspired tensor network methods to enhance the efficiency of classical image processing and optical simulations, such as wave-front propagation and image formation.
This paper demonstrates that the seeding method in NVT molecular dynamics simulations effectively tests classical nucleation theory (CNT) for Lennard-Jones condensation in small systems, finding that CNT accurately predicts stable cluster radii and aligns well with the critical cluster properties of infinite systems when using robust equations of state.
This paper presents a mesh-free, scalable Direct Simulation Monte Carlo (DSMC) method for the spatially homogeneous multispecies Landau equation that utilizes a regularized scattering kernel to efficiently handle realistic mass ratios and preserve physical invariants.
This paper develops and benchmarks four machine-learned interatomic potentials (ACE, MACE, GAP, and tabGAP) trained on DFT data to enable accurate, large-scale molecular dynamics simulations of radiation-induced defects and oxygen stoichiometry in YBCO high-temperature superconductors.
This paper proposes a physics-informed framework that couples Kolmogorov-Arnold Networks (KANs) with a level-set formulation to efficiently and accurately solve complex moving boundary problems, such as temperature distribution and interface evolution, without requiring experimental data.