An improved neural network method for solving the Schrödinger equation

Abstract

We propose a neural network (NN) based algorithm for calculating vibrational energies and wave functions and apply it to problems in 2-, 4-, and 6-dimensions. By using neurons as basis functions and methods of nonlinear optimization, we are able to compute three states of a 6-D Hamiltonian using only 50 basis functions. In a standard direct product basis, thousands of basis functions would be necessary. Previous NN methods for solving the Schrödinger equation computed one level at a time and optimized all of the parameters using expensive nonlinear optimization methods. Using our approach, linear coefficients in the NN representation of wave functions are determined with methods of linear algebra and many levels are computed at the same time from one set of nonlinear NN parameters. In addition, we use radial basis function neurons to ensure the correct boundary conditions. The use of linear algebra methods makes it possible to treat systems of higher dimensionality.