119 papers
This paper proposes the Taguchi method as an efficient optimization tool for nonlinear pulse propagation in optical fibers, demonstrating its rapid convergence and effectiveness through applications to guiding center solitons and soliton order conservation in dispersion-decreasing fibers.
This paper introduces a multitask surrogate model for neutron star equations of state that achieves near-perfect physical validity classification and high-precision regression of key stellar properties while providing distribution-free, certified uncertainty estimates through the novel application of Mondrian conformal prediction.
This paper presents an extended non-affine deformation theory incorporating a time-dependent memory kernel within the Generalized Langevin Equation, which successfully predicts the viscoelastic response of poly(methyl methacrylate) across twenty frequency decades and validates these findings against diverse experimental and computational methods.
This paper demonstrates that combining machine learning with gold-standard CCSD(T) electronic structure theory enables the first fully converged, quantitative prediction of the ion pairing free energy for CaCO in water, resolving long-standing challenges in accurately capturing enthalpic and entropic effects for complex aqueous systems.
This paper establishes a microscopic framework for non-Markovian phonon dynamics driven by high-intensity THz pulses, using molecular dynamics simulations to derive noise and dissipation kernels that quantify heat production and reveal the crossover between Markovian and non-Markovian regimes on picosecond timescales.
This paper introduces "paces," a GPU-optimized parallel algorithm that computes quantum dynamics by constructing and evolving a restricted subspace via repeated Hamiltonian applications, offering an efficient alternative to matrix-product states for sparse Hamiltonians.
This paper presents the first successful *ab initio* simulation of non-equilibrium recombination in evolving ultracold plasmas by employing a co-moving reference frame and trajectory-based identification criteria, achieving a 20% recombination efficiency that aligns with laboratory measurements.
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 establishes a formal equivalence between the Lindblad equation and Path Integral Molecular Dynamics (PIMD), demonstrating how PIMD can be utilized to simulate the non-equilibrium time evolution and convergence of ensemble-averaged observables in large atomic systems without explicitly solving the Lindblad equation, while ensuring the physical consistency of the density operator's positivity.
This paper presents an ab initio quantum embedding workflow that derives interacting flat-band Hamiltonians for magic angle twisted bilayer graphene, revealing robust insulating states at and while uncovering a fragile semimetal with Kekulé modulation at and explaining the resulting particle-hole asymmetry through momentum-dependent single-particle renormalizations.
This paper introduces the Variable Coherence Model (VCM) to simulate free-electron laser pulses, demonstrating that a variable coherence width parameter enables continuous control over pulse noise and sub-pulse statistics while maintaining fixed average parameters, thereby bridging the gap between maximally random and fully coherent regimes for applications such as absorption simulations.
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 proposes and validates a simplified, trajectory-dependent local model for electronic stopping of light ions (hydrogen and helium) in tungsten, demonstrating its efficiency and physical transparency over complex tensorial formulations for atomistic simulations of energy dissipation.
This paper presents a scalable, two-step framework that propagates chemical kinetics parameter uncertainties onto reduced manifolds to enable efficient, spatially resolved uncertainty quantification in high-fidelity reacting flow simulations, revealing significant variability in trajectory and equilibrium times driven by mixing and low-to-intermediate temperature chemistry.
This paper introduces a gauge transformation that eliminates time-dependent onsite potentials by renormalizing hopping terms with phase factors, thereby simplifying time-ordered integrals and facilitating the simulation of pulse propagation in scattering systems.
This paper introduces an extension of localized intrinsic bond orbitals (IBOs) to decode correlated charge migration dynamics, successfully mapping complex many-electron behaviors to intuitive chemical concepts like curly arrows and hyperconjugation while identifying key mechanisms and efficient molecules through high-level TDDMRG simulations.
This paper presents the public release of OpenGadget3, a cosmological hydrodynamical N-body code featuring a robust implementation of self-interacting dark matter that supports various elastic scattering cross-sections and two-species models, validated through accuracy tests and performance scaling.
This paper demonstrates that the optical extinction manifold of dielectric materials exhibits intrinsic sparsity best captured by the Discrete Cosine Transform rather than the FFT, enabling a compressed sensing architecture that achieves high-fidelity material reconstruction with a 51–94% reduction in hardware sensors by overcoming traditional Nyquist limits.
This paper introduces an efficient preconditioning method derived from the metric tensor to accelerate gradient-based optimization of infinite projected entangled pair states (iPEPS), significantly improving computational efficiency and convergence for simulating strongly correlated quantum systems like the Heisenberg and Kitaev models.