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.
The paper introduces a scalable, modular hybrid framework combining machine-learned intralayer potentials with physics-based interlayer interactions to accurately and efficiently model the complex structural and thermodynamic properties of large-scale van der Waals heterostructures with near *ab initio* precision.
This paper introduces the first non-Euclidean neural quantum state ansatz using hyperbolic GRUs, demonstrating that they match or outperform conventional Euclidean RNNs in approximating ground state energies, particularly in quantum spin systems with hierarchical interaction structures.
By combining universal machine-learning interatomic potentials with an advanced force constant-based dimensionality classification method, researchers systematically screened nearly 36,000 materials to discover over 9,000 novel low-dimensional structures, including 887 potentially exfoliable 2D sheets, that were previously overlooked by conventional geometric descriptors.
This paper demonstrates that in periodically forced mean-field Ginzburg-Landau models, the correct conjugate field at the critical period is the even-Fourier component of the applied field, which governs a universal order parameter scaling of and reveals a distinct parity-dependent scaling rule for mode-resolved deviations.
This paper enhances Clifford Data Regression for error mitigation in quantum chemistry simulations by introducing Energy Sampling to optimize training circuit selection and Non-Clifford Extrapolation to better model noise evolution, both of which outperform the original method in noisy VQE experiments.
This paper introduces Harmonic and Mixed Path Integral Monte Carlo (H-PIMC and M-PIMC) algorithms that combine harmonic sampling with the worm algorithm to significantly improve acceptance ratios, reduce autocorrelation times, and accelerate convergence for simulating quantum condensed phases, particularly in solids and dense confined liquids.
This paper demonstrates that an artificial neural network can serve as a high-fidelity surrogate model for TALYS-2.0, accelerating the generation of charged-particle residual product cross sections by over 1000 times while enabling efficient multi-parameter adjustments to improve agreement with experimental data.
This paper introduces a Generalized Onsager-Regularized Lattice Boltzmann Method that utilizes fully local corrections to eliminate Navier-Stokes modelling errors on standard lattices, achieving exact solutions and significantly improved accuracy and stability compared to existing schemes.
This paper presents a unified, physics-constrained neural-operator framework that accelerates Direct Simulation Monte Carlo simulations by replacing the Variable Hard Sphere model with a stochastic neural collision kernel for improved generalization and by introducing an efficient surrogate for ab initio Jäger potentials, collectively achieving high-fidelity predictions of rarefied gas dynamics with reduced computational cost.
This study demonstrates that ab initio electronic structure calculations for various molecules exhibit Gaussian orthogonal ensemble statistics characteristic of random matrix theory, confirming its universality in describing complex molecular spectra and predicting specific magnetic field dependencies that induce a transition to Gaussian unitary ensemble behavior at extreme scales.