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 proposes a mechanism for nonvolatile electrical manipulation of spin and layer degrees of freedom in altermagnetic bilayers, such as CuF2, by utilizing sliding ferroelectricity to reversibly switch d-wave altermagnetic spin splitting, thereby enabling multifunctional spin-layertronic devices with potential multi-state logic applications.
This paper introduces a paradigm shift in Quantum Monte Carlo simulations by replacing the partition function with a generalized reduced-density-matrix (GRDM) as the sampled object, thereby overcoming measurement bottlenecks to enable the direct and efficient extraction of off-diagonal dynamical observables and mixed-state correlators.
This paper presents a unified Bayesian optimization framework using Gaussian processes with derivative observations and advanced extensions like Optimal Transport and random Fourier features to efficiently accelerate the search for minima and saddle points on potential energy surfaces, bridging theoretical formulation with practical implementation through accompanying Rust code.
This paper critically evaluates a quantum-computing stochastic series expansion method for mitigating the sign problem, demonstrating that while constant energy shifts do not strictly resolve the issue for non-commuting Hamiltonians, they effectively suppress negative weights and balance statistical accuracy when moderate shift parameters are applied.
SymBoltz.jl is a new Julia package that provides a symbolic-numeric, approximation-free, and fully differentiable solver for linear Einstein-Boltzmann equations, offering exact derivatives and flexible model construction while maintaining performance comparable to existing approximation-based codes.
This paper presents a computational framework that combines database analysis with microscopic modeling to predict and identify promising new molecular single-photon emitters, successfully benchmarking the approach on dibenzoterrylene and discovering novel candidates like a chiral emitter.
This paper benchmarks various parallelization strategies for flat-histogram Monte Carlo sampling and demonstrates that non-uniform energy domain decomposition offers the most significant performance improvements, providing concrete recommendations for optimizing simulations of complex alloy systems.
This paper presents and validates a generalized algorithm for efficient Monte-Carlo sampling of metastable systems using non-local updates in collective-variable space under underdamped Langevin dynamics, demonstrating substantial performance improvements over previous overdamped approaches and extending the applicability of machine-learning-based samplers to more realistic molecular systems.
This paper demonstrates that the Transient Time Correlation Function (TTCF) method is a computationally efficient and precise alternative to traditional time-averaging approaches for calculating nonequilibrium transport coefficients in both linear and nonlinear regimes, as validated through case studies of the Lorentz gas and anharmonic oscillator chains.
This paper introduces a novel tensor-network methodology that combines real-space Bethe-Salpeter Hamiltonian encoding with a Chebyshev algorithm to efficiently compute excitonic spectra and bound-exciton spectral functions in super-moiré systems exceeding one billion lattice sites, thereby overcoming the computational limitations of conventional approaches for large-scale quantum matter.