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.
The paper introduces UniMatSim, a modular Python framework that unifies diverse universal machine learning interatomic potentials to automate high-throughput materials simulations, demonstrated by successfully screening thousands of candidates to identify stable 2D Lieb-lattice structures with specific magnetic band characteristics.
This paper presents a first-principles density functional theory study of erbium point defects in 4H-SiC, providing materials-level support for their development as a scalable platform for quantum devices such as single-photon emitters and distributed quantum computing networks.
This self-contained tutorial introduces the variational Monte Carlo method utilizing artificial neural network-based trial wave functions by covering their historical context, mathematical foundations, and practical applications to various potentials and simple molecular systems.
This paper proposes a lightweight, post-hoc method that generates interpretable, pointwise error estimates for Physics-informed Neural Networks (PINNs) by solving an error equation via finite difference methods using the PINN's PDE residual, thereby enhancing trust in their predictions without requiring knowledge of the true solution.
This paper introduces a novel hybrid Waveguide Neural Operator (WGNO) and evaluates Physics-Informed Neural Networks (PINNs) for simulating Extreme Ultraviolet (EUV) wave diffraction from lithography masks, demonstrating that these neural systems achieve state-of-the-art accuracy with significantly faster inference times and strong generalization compared to traditional numerical solvers.
This perspective argues that physics-informed tensor networks, which leverage both symmetry-induced block-sparsity and complementary efficiency strategies like hybrid architectures, provide unifying frameworks for scalable quantum simulation, computation, and machine learning.
This paper introduces RadField3D, an open-source Geant4-based Monte Carlo simulation tool and a compatible machine-interpretable data format with a Python API, designed to generate 3D radiation field datasets for advancing deep learning research in medical radiation-protection dosimetry.
This paper presents a GPU-accelerated implementation of the sharp-interface immersed boundary solver ViCar3D using OpenACC, CUDA Fortran, and MPI, which achieves a 20-fold speedup and high scalability on multi-GPU systems to enable large-scale simulations of complex 3D flows with up to 200 million mesh points.
This study proposes an intercalation-driven paradigm using Lithium and Vanadium in bilayer V2Se2O to transform altermagnets into room-temperature ferrimagnetic-ferroelastic multiferroics with enhanced spin splitting, half-metallicity, and giant magnetoresistance, thereby enabling advanced spintronic applications.
This paper introduces a resource-efficient scheme using step-dependent cyclic quantum walks to simulate diverse topological phenomena, including robust edge states, topological flat bands, and phase transitions, on cyclic graphs without requiring complex split-step protocols.