Combining ab initio computations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules

Abstract

We describe a new method to calculate the vibrational ground state properties of weakly bound molecular systems and apply it to (HF)2 and HF–HCl. A Bayesian Inference neural network is used to fit an analytic function to a set of ab initio data points, which may then be employed by the quantum diffusion Monte Carlo method to produce ground state vibrational wave functions and properties. The method is general and relatively simple to implement and will be attractive for calculations on systems for which no analytic potential energy surface exists.