1258 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 derives explicit integral formulas and closed-form expressions for antisymmetric Gaunt coefficients to simplify Vector Spherical Tensor Products, achieving a ninefold reduction in computational cost and enabling efficient implementations for SO(3)-equivariant neural networks.
This paper introduces NATPS, a novel method that combines the time-reversible Mapping Approach to Surface Hopping (MASH) dynamics with transition path sampling to efficiently simulate rare nonadiabatic events and provide mechanistic insights into photochemical processes while significantly reducing computational costs compared to brute-force approaches.
This paper introduces \texttt{featom}, an open-source, high-order finite element Fortran code that achieves high-accuracy solutions for radial Schrödinger, Dirac, and Kohn-Sham equations in heavy atoms through systematic convergence control and specialized techniques for handling singularities, while demonstrating significant speed improvements over existing methods.
This paper demonstrates that liquid condensates within cells can achieve directed movement and spatial organization through reaction-driven diffusiophoresis, where local biochemical fuel consumption and waste production generate concentration gradients that induce incompressibility-driven fluxes and asymmetric reactions to propel the droplets.
This paper introduces FourierSpecNet, a hybrid deep learning framework that integrates the Fourier spectral method to efficiently approximate the Boltzmann collision operator, achieving resolution-invariant learning, zero-shot super-resolution, and significant computational savings while maintaining accuracy across elastic and inelastic collision regimes.
This paper introduces an efficient preconditioning method derived from the metric tensor to accelerate gradient-based optimization of infinite projected entangled pair states (iPEPS), significantly improving computational efficiency and convergence for simulating strongly correlated quantum systems like the Heisenberg and Kitaev models.
This paper introduces a novel two-dimensional visualization framework that maps reaction trajectories onto a permutation-corrected RMSD plane with gradient-enhanced Gaussian Process energy interpolation, enabling more effective comparison and validation of optimization histories across different computational methods for complex chemical reactions.
The paper introduces "El Agente Cuántico," a multi-agent AI system that automates complex quantum simulation workflows by translating natural-language scientific intent into validated computations across diverse software frameworks, thereby lowering technical barriers and enabling more autonomous exploration of quantum systems.
This paper demonstrates that the failure of the quantum-inspired Matrix Product State approach to solve 3-SAT via imaginary time propagation is fundamentally caused by classical computational complexity, specifically the hardness of the #3-SAT counting problem, which manifests as an entanglement barrier and necessitates superlinear non-stabilizer resources.
This paper introduces a semidefinite machine learning framework that combines input convex neural networks with semidefinite programming to learn a data-driven, vertex-based approximation of the -representable two-electron reduced density matrix (2-RDM) boundary, enabling direct variational calculations with accuracy comparable to higher-order positivity constraints but at the computational cost of two-positivity methods.