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 paper introduces "Quantum Neural Physics," a hybrid quantum-classical framework that maps discretized partial differential equations into parameter-free quantum convolutional kernels with logarithmic circuit depth, enabling efficient and accurate solutions for complex physical problems like the Navier-Stokes equations on quantum simulators.
This paper presents a highly accurate and scalable Graph Neural Network potential for aluminum, developed through a sequential-refinement workflow, which enables million-atom simulations to reveal how stacking-fault energetics and diffusion critically influence solidification microstructures and mechanical properties, outperforming both classical and general-purpose machine learning potentials.
This paper introduces soliton_solver, an open-source, GPU-accelerated Python package that utilizes a modular, theory-agnostic architecture to efficiently simulate and visualize topological solitons across diverse two-dimensional non-linear field theories.
This paper demonstrates a novel method for reconstructing the 3D electron density of tumbling proteins by utilizing ion signatures from Coulomb explosions to determine molecular orientations, achieving angular accuracy and resolution comparable to or better than conventional diffraction-only techniques in single-particle X-ray imaging.
This study establishes two power law relations for the thermal slip length at normal liquid/solid interfaces by analyzing the in-plane structure and vibrational frequency of the contact zone in Lennard-Jones systems, revealing that enhanced translational order and frequency matching significantly reduce thermal impedance.
This study establishes the crystallographic origins of ferroelectricity in heterodeformed bilayer hexagonal boron nitride across both small and large twist angles, revealing distinct polarization switching mechanisms involving swirling dislocations and introducing a novel density-functional-theory-informed continuum framework (BFIM) to accurately predict ferroelectric behavior in large-unit-cell heterostructures where traditional methods fail.
This study extends the investigation of critical collapse in axisymmetric massless complex scalar fields to higher angular modes () using a novel -cartoon symmetry reduction, revealing that while discrete self-similarity and universality persist within each mode, the critical exponents depend explicitly on and extremal black holes are excluded at the threshold.
This paper presents a mathematically consistent reduced-order model coupling Kirchhoff-Love plate bending with Reynolds lubrication theory, implemented via a monolithic interior-penalty finite element scheme in FEniCSx, to simulate and analyze the nonlinear fluid-structure interaction dynamics of wafer-to-wafer bonding.
This study reveals that interlayer interactions in bilayer V2S2O significantly modulate valence band structures and suppress piezomagnetism, while gate-voltage modulation induces asymmetric control over spin-polarized transport in Au/V2S2O/Au devices, offering critical insights for optimizing multilayer altermagnetic spintronics.
This study demonstrates that fine-tuning universal machine-learning interatomic potentials on systematically generated structures enables near-DFT accuracy in predicting mixing energies for 2D high-entropy alloys, overcoming the computational limitations of direct DFT calculations for complex systems like experimentally synthesized (Mo,Ta,Nb,W,V)S.