1205 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 MultiAtomLiouvilleEquationGenerator (MulAtoLEG), an open-source Mathematica package that efficiently generates exact Liouville superoperators and master equations for multilevel atomic systems and general Hamiltonians by leveraging vectorization and sparse linear algebra.
This paper introduces a foundational implicit solvent model that distills evolutionary knowledge from the protein language model ESM3 into a graph neural network, enabling accurate and stable molecular dynamics simulations of both folded and intrinsically disordered proteins.
This paper adopts the integrable Pullin equation to analytically derive explicit relaxation rates and transport coefficients for polyatomic gases, rigorously confirming the dependence of thermal conductivity on thermal non-equilibrium degrees of freedom and proposing a novel Rykov-type model that accurately captures translational-rotational heat flux interactions.
This paper proposes a Residual Attention Physics-Informed Neural Network (RA-PINN) framework that integrates unified five-field operators with residual connections and attention mechanisms to achieve superior accuracy and robustness in simulating complex, nonlinear steady-state electrothermal multiphysics systems compared to existing PINN architectures.
This paper introduces and validates the "koopmon" method, a novel quantum-classical Hamiltonian model based on Koopman wavefunctions that successfully captures correlation effects and accurately reproduces fully quantum dynamics in Rashba spin-orbit coupling systems, outperforming conventional Ehrenfest approaches particularly in orbital dynamics and harmonic potential scenarios.
This paper presents a self-consistent numerical framework that integrates ion-energy-dependent secondary electron emission into circuit-plasma co-simulation via both strict and weak coupling schemes, enabling predictive modeling of voltage breakdown in high-voltage pulsed vacuum systems where traditional models fail.
This paper introduces two field optimization techniques, utilizing reduced bit-width representations and interpolation, to significantly enhance memory efficiency in time-reversible gradient computations for the open-source, GPU-accelerated FDTDX solver, thereby enabling more scalable nanophotonic inverse design simulations.
This paper derives the conserved energy-like quantity and ensemble measure for Martyna--Tobias--Klein barostats with restricted cell degrees of freedom, demonstrating that the standard formulation adapts by replacing the total degrees of freedom with the number of active axes to ensure exact conservation and correct sampling of the restricted isothermal--isobaric ensemble.
This study reveals that collective many-body electronic polarization, rather than simple charge transfer, drives the spontaneous negative charging of oil droplets in water by creating an asymmetric interfacial electron density and distinct hydrogen-bond motifs at the decane-water interface.
This paper proposes a hybrid digital-thermodynamic algorithm that significantly accelerates thermodynamic computing by using a classical processor to compute optimized initial states—inspired by the Mpemba effect—that suppress slow relaxation modes, thereby reducing the thermalization time required for matrix operations like inversion and determinant calculation.