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 DD-13M, a novel 4-D trajectory database containing over 26,000 complete ligand-protein dissociation processes, and utilizes it to train UnbindingFlow, a deep generative model capable of predicting dissociation dynamics and rate constants for new drug targets.
This study introduces scalable machine learning models trained on a large dataset of over 400,000 configurations to predict quantum transport properties in disordered 2D hexagonal materials, demonstrating that while geometry-driven features enable strong in-domain accuracy, tree-based models struggle with extrapolation, thereby highlighting the need for physics-informed architectures for robust generalization in nanodevice design.
This study analyzes the boundary-velocity error and numerical stability of the accelerated multi-direct-forcing immersed boundary method to identify a critical parameter governing stability and an optimal acceleration parameter that minimizes velocity errors independently of boundary details, thereby providing guidelines for stable and accurate moving boundary simulations.
Using time-dependent density functional theory, this study demonstrates that laser incidence direction controls ultrafast net magnetization in g-wave altermagnets like CrSb by inducing asymmetric sublattice demagnetization via anisotropic optical intersite spin transfer, a mechanism distinct from d-wave altermagnets and governed by the alignment of laser polarization with spin-uncompensated electronic regions.
This study demonstrates that the neural canonical transformation (NCT) approach, when applied to atomic coordinates, successfully calculates the ground and excited vibrational spectra of the highly fluxional CH5+ molecule by effectively capturing its anharmonic effects and nuclear quantum delocalization.
This paper generalizes the interpolative separable density fitting (ISDF) method by integrating adaptive real-space grids and a dual-space multilevel kernel-splitting algorithm to efficiently compress electron repulsion integrals for highly localized basis functions, thereby enabling scalable many-body electronic structure simulations for complex molecular systems that are intractable with uniform grids.
This paper introduces a stochastic cluster expansion framework that enables near-DMRG accuracy for total correlation energies in large condensed-phase systems by combining exactly treated subspaces with randomly sampled environment orbitals, thereby eliminating the need for prior active space selection and facilitating high-accuracy studies of chemical processes in complex environments.
This paper presents numerical evidence using Clean Numerical Simulation suggesting that the Navier-Stokes equations may admit distinct global solutions arising from initial conditions differing by as little as , thereby challenging the uniqueness of smooth solutions and offering new insights into the associated Millennium Prize Problem.
This paper introduces StrAPS, an automated method that utilizes the rotationally invariant angular power spectrum of the 3D structure factor to robustly discriminate between known block copolymer morphologies and identify novel structures without requiring prior enumeration or manual expertise.
This paper presents a fault-tolerant quantum algorithmic framework for simulating phase-contrast transmission electron microscopy image formation, which leverages quantum Fourier transforms to efficiently model wave propagation and specimen interactions while offering quantum advantages for specific Fourier-space queries and global statistics despite the measurement overhead required for full image reconstruction.