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 demonstrates that transforming molecular Hamiltonians to the adiabatic representation to eliminate spin-orbit coupling inadvertently generates significant non-adiabatic couplings that must be explicitly included to avoid severe errors in rovibronic predictions, providing exact conditions for validity and practical diagnostics for when single-state approximations fail.
Using molecular dynamics based on density functional theory, this study reveals that the complex high-pressure crystal phases of methane arise from the packing of specific supermolecular clusters (icosahedral and polyhedral) where a trade-off between efficient packing and suppressed rotational entropy, driven by orientation-dependent intermolecular interactions, explains the observed non-cubic symmetries and sluggish phase transitions.
This paper introduces machine-learning potentials incorporating environment-dependent long-range electrostatic charges that improve training accuracy for diverse systems and enable the prediction of key dielectric properties, such as LO-TO splitting and dielectric constants, using only energy, force, and stress data.
This paper reports the discovery of pressure-induced band gap widening in carbon-chain-filled KFI zeolite and the synthesis of ultra-long cumulene chains within this framework, which exhibit a record-breaking superconducting transition temperature of approximately 62 K, offering new pathways for high-pressure semiconducting electronics and high-temperature superconductivity.
This paper provides a comprehensive comparison of explicit relativistic particle pushers used in Particle-in-Cell simulations, demonstrating that a significant class of these schemes can be generalized to arbitrary high-order accuracy and evaluating the performance of their fourth-order variants against second-order counterparts.
This study employs advanced GAP interatomic potentials, molecular dynamics, and solid-state nudged elastic band calculations to elucidate the atomic-scale mechanisms and pressure-dependent nucleation barriers driving the phase transformations of silicon, particularly linking simulation results to experimental observations of hexagonal phase formation from BC8/R8 precursors.
This paper presents an exact frequency-domain formulation of pendulum-like systems that unifies oscillatory, separatrix, and rotational regimes under a single universal spectral kernel, revealing that regime transitions are symmetry-driven reorganizations in frequency space rather than changes in the underlying spectral structure.
This study introduces a factorized-implicit Fourier Neural Operator (F-IFNO) framework that enhances long-term stability and accuracy in predicting three-dimensional turbulence by integrating uncertainty quantification and error propagation analysis to overcome the limitations of traditional models and existing neural operators.
This paper presents a quadrature-based model reduction framework for Lagrangian hydrodynamics that utilizes a strongly energy-conservative variant of the empirical quadrature procedure to achieve near machine-precision total energy conservation while maintaining accuracy comparable to standard methods.
This paper introduces B-ODIL, a Bayesian extension of the Optimization of a Discrete Loss (ODIL) method that integrates PDE-based prior knowledge with data likelihood to solve inverse problems with quantified uncertainties, demonstrating its effectiveness through synthetic benchmarks and a clinical application for estimating brain tumor concentration from MRI scans.