Variational reproducing kernel Hilbert space (RKHS) grid method for quantum mechanical bound-state problems

Solution of the quantum fluid dynamical equations with radial basis function interpolation

The paper proposes a numerical technique within the Lagrangian description for propagating the quantum fluid dynamical (QFD) equations in terms of the Madelung field variables R and S, which are connected to the wave function via the transformation psi = exp(R + iS)/[symbol: see text]. The technique rests on the QFD equations depending only on the form, not the magnitude, of the probability density rho = magnitude of psi 2 and on the structure of R = [symbol: see text]/2 ln rho generally being simpler and smoother than rho. The spatially smooth functions R and S are especially suitable for multivariate radial basis function interpolation to enable the implementation of a robust numerical scheme. Examples of two-dimensional model systems show that the method rivals, in both efficiency and accuracy, the split-operator and Chebychev expansion methods. The results on a three-dimensional model system indicates that the present method is superior to the existing ones, especially, for its low storage requirement and its uniform accuracy. The advantage of the new algorithm is expected to increase for higher dimensional systems to provide a practical computational tool.

Solving the bound-state Schrodinger equation by reproducing kernel interpolation

Based on reproducing kernel Hilbert space theory and radial basis approximation theory, a grid method is developed for numerically solving the N-dimensional bound-state Schrodinger equation. Central to the method is the construction of an appropriate bounded reproducing kernel (RK) Lambda(alpha)( ||r ||) from the linear operator -nabla(2)(r)+lambda(2) where nabla(2)(r) is the N-dimensional Laplacian, lambda>0 is a parameter related to the binding energy of the system under study, and the real number alpha>N. The proposed (Sobolev) RK Lambda(alpha)(r,r(')) is shown to be a positive-definite radial basis function, and it matches the asymptotic solutions of the bound-state Schrodinger equation. Numerical tests for the one-dimensional (1D) Morse potential and 2D Henon-Heiles potential reveal that the method can accurately and efficiently yield all the energy levels up to the dissociation limit. Comparisons are also made with the results based on the distributed Gaussian basis method in the 1D case as well as the distributed approximating functional method in both 1D and 2D cases.

CONSTRUCTINGMULTIDIMENSIONALMOLECULARPOTENTIALENERGYSURFACES FROMABINITIODATA

This paper describes the reproducing kernel Hilbert space (RKHS) method for constructing accurate, smooth, and efficient global potential energy surface (PES) representations for polyatomic systems using high-level ab initio data. The RKHS method provides a rigorous and effective framework for smooth multivariate interpolation of arbitrarily scattered data points and also for incorporating various physical requirements onto the PESs. Smoothness, permutation symmetry, and the asymptotic properties of polyatomic systems can be incorporated into the construction of reproducing kernels to render globally accurate PESs. Tensor products of one-dimensional generalized-spline-reproducing kernels are amenable to a fast algorithm, which makes a single evaluation of RKHS PESs essentially independent of the number of interpolated ab initio data points. This efficient implementation enables the study of the detailed dynamics of polyatomic systems based on high-quality RKHS PESs.