119 papers
The paper introduces a highly efficient one-way shooting algorithm for transition path sampling in overdamped stochastic systems that guarantees the acceptance of every proposed reactive trajectory through a reweighting scheme, thereby enabling the effective study of difficult-to-access processes like CO clathrate hydrate formation.
The paper introduces SAP-X2C, a computationally efficient two-component relativistic Hamiltonian that incorporates two-electron picture-change effects via Lehtola's superposition of atomic potentials to achieve accuracy comparable to complex mean-field methods while maintaining size-intensivity for extended systems.
This paper extends the Theory of Porous Media to model non-isothermal material injection into porous structures, specifically for percutaneous vertebroplasty, by incorporating local thermal non-equilibrium conditions and demonstrating thermodynamic consistency through numerical simulations.
This paper introduces the Weighted Planar Stochastic Porous Lattice (WPSPL), a multifractal, scale-free porous substrate, and demonstrates through analytical and numerical methods that bond percolation on this lattice exhibits a continuous family of distinct universality classes with critical exponents that vary with porosity while satisfying the Rushbrooke inequality.
This paper introduces a geometric framework of Covariant Multi-Scale Negative Coupling on dynamic Riemannian manifolds to counteract dimensional reduction in dissipative PDEs, theoretically proving the finite dimensionality of global attractors while numerically validating the mechanism's ability to stabilize high-dimensional structural complexity against macroscopic dissipation.
This paper introduces NATPS, a novel method that combines the time-reversible Mapping Approach to Surface Hopping (MASH) dynamics with transition path sampling to efficiently simulate rare nonadiabatic events and provide mechanistic insights into photochemical processes while significantly reducing computational costs compared to brute-force approaches.
This paper demonstrates that the macroscopic behavior of non-equilibrium two-phase flow in porous media can be successfully predicted by mapping droplet distributions onto an equilibrium spin-glass model derived via machine learning and the maximum entropy principle, revealing that the transition to a glassy flow regime with hysteresis and non-linear dynamics coincides with the spin-glass phase transition.
This paper presents a data reduction method and validates the CSNS in-house Monte Carlo code, Prompt, by demonstrating its ability to accurately reproduce thermal neutron scattering experiments on light and heavy water, including the elimination of inelasticity effects and the analysis of multiple scattering.
This paper proposes a computationally efficient, sensitivity-free data-driven topology optimization framework that overcomes the limitations of high-information-entropy initialization and expensive simulations by integrating a mesh-independent mutation module and a non-AI rapid identification algorithm to effectively solve strongly nonlinear and non-differentiable engineering design problems.
This paper presents a general numerical framework that combines weighted interpolation with a conservative flux mapping algorithm to accurately reconstruct interfaces and transfer data between partitioned multiphysics domains, achieving high geometric and flux conservation accuracy across various applications.
This paper presents a robust semi-analytical pseudo-spectral method utilizing mapped associated Legendre polynomials and an advanced exponential time differencing scheme to efficiently and accurately simulate rotating, stratified flows in unbounded cylindrical domains by overcoming the numerical stiffness typically caused by strong shear and fast wave forces.
This paper demonstrates that machine learning models trained to predict the two-electron reduced density matrix (2-RDM) can accurately surrogate correlated wavefunction methods, enabling coupled-cluster-quality electronic structure calculations for large solvated systems at a fraction of the conventional computational cost.
This paper demonstrates that Oscillator Ising Machines outperform Bistable Latch Ising Machines in solving combinatorial optimization problems because the configuration-dependent stability of oscillators allows for the selective destabilization of high-energy states, whereas the uniform stability of latches limits their computational efficiency.
This paper introduces a constant-lag Neural Delay Differential Equations (NDDEs) framework, inspired by the Mori-Zwanzig formalism, to effectively learn non-Markovian dynamics from partially observed data by identifying memory effects through time delays, demonstrating superior performance over existing methods like LSTMs and ANODEs across synthetic, chaotic, and experimental datasets.
This paper introduces Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for Machine Learning Interatomic Potentials, demonstrating that element-wise routing with shared nonlinear experts achieves state-of-the-art accuracy across multiple benchmarks while revealing chemically interpretable specialization aligned with periodic-table trends.
This study presents a machine learning framework for predicting steady-state flow through porous media, demonstrating that the Fourier Neural Operator (FNO) outperforms convolutional autoencoders and U-Nets by achieving high accuracy, significant computational speedups over traditional CFD, and mesh-invariant properties ideal for topology optimization.
This paper derives explicit integral formulas and closed-form expressions for antisymmetric Gaunt coefficients to simplify Vector Spherical Tensor Products, achieving a ninefold reduction in computational cost and enabling efficient implementations for SO(3)-equivariant neural networks.
This paper introduces a Physics-Informed Neural Operator (PINO) surrogate model that accelerates the retention analysis of Ferroelectric Vertical NAND devices by over 10,000 times compared to traditional TCAD simulations while maintaining physical accuracy, thereby enabling efficient optimization of device designs against charge detrapping and ferroelectric depolarization.
This paper presents a GPU-accelerated transient Electromagnetic-Thermal-Mechanical co-simulation solver that enables full-scale, non-homogenized early-stage design of advanced packages, overcoming the limitations of conventional steady-state methods by accurately capturing dynamic signal-induced stress and thermal events to prevent costly late-stage failures.
This paper introduces "differentiable microscopy" (), a data-driven, top-down design framework that automatically optimizes optical systems for phase retrieval, demonstrating superior performance over existing methods and experimentally validating its effectiveness on biological samples.