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 introduces LOCABINACONN, a computationally efficient local methodology that transforms continuous inverse-designed all-dielectric structures into manufacturable 3D-printable devices using only resin and air, thereby enabling the synthesis of large-scale optimized devices without prohibitive simulation costs.
This paper proposes and validates a general linear correction method that resolves the intrinsic energy ambiguity in DFT+X approaches, thereby establishing them as fully first-principles tools capable of accurately predicting phase stability and discovering new intermetallic compounds in uranium-based alloys and other diverse systems under high pressure.
This study employs machine learning potentials and molecular dynamics simulations to elucidate the deformation mechanisms and compressive response of NbTaTiZr refractory multi-principal element alloys, revealing significant anisotropy in yield strength and twinning behavior across crystal orientations, a strain-rate-dependent transition from dislocation slip to structural disordering, and the compositional influence of Nb/Ta versus Ti/Zr on mechanical performance.
This study employs a machine-learning-enhanced computational framework to reveal that while Au-rich Pd-Au-Cu surfaces suppress adsorption overall, the specific mitigation of CO poisoning by Cu arises from its ability to provide viable pathways for hydrogen absorption into the material when Pd-dominated paths are blocked.
This study combines thermodynamic analysis and molecular dynamics simulations to reveal that hydrogen and helium synergistically drive the stress-induced elastic-plastic deformation and dynamic evolution of core-shell structured cavities in BCC-Fe under fusion reactor conditions.
This paper presents a multi-channel phase space generation method enhanced by Feynman-diagram-gauge amplitudes to accurately simulate challenging high-energy lepton collider processes in the SMEFT, specifically addressing lepton-mass singularities through specialized phase-space parametrization and modifications to the HELAS library for precise evaluation of vertices at very small invariant momentum squares.
This paper introduces NEP-CG and NEP-AACG, efficient neuroevolution potential frameworks that generate low-noise coarse-grained training data to achieve high accuracy and transferability across diverse systems, including liquid water and gold nanowires, while enabling multiscale simulations at speeds of hundreds to thousands of nanoseconds per day on consumer-grade GPUs.
This paper presents the major updates to the PHYSBO library in versions 2 and 3, which prioritize enhanced usability, portability, and compatibility with modern computing environments over new algorithmic developments to better support Bayesian optimization in physics and materials research.
This study demonstrates that a vortex spin-torque oscillator with a modified free layer (m-VSTO) achieves significantly enhanced information processing capacity and reduced power consumption for physical reservoir computing by operating in a stable, low-power regime with long transients rather than at the edge of chaos.
This paper presents a robust inverse reconstruction framework that integrates scattering theory, Lorentz oscillator modeling, and nonlinear effective medium approximations to accurately retrieve the broadband complex permittivity, constituent composition, and microstructural topology of heterogeneous optical composites from a single infrared extinction spectrum, thereby overcoming the limitations of conventional linear unmixing methods.