Deep learning methods for the computation of vibrational wavefunctions

TL;DR

This paper introduces deep learning models trained on analytical Hamiltonians to accurately generate vibrational wavefunctions for complex molecular systems without relying on physical model assumptions.

Contribution

It presents a novel data-driven approach using neural networks to generalize vibrational wavefunctions across different Hamiltonians in quantum chemistry.

Findings

Successfully reproduces excited vibrational wavefunctions

Models generalize to complex Hamiltonians

Versatile approach applicable to various molecular systems

Abstract

In this paper we design and use two Deep Learning models to generate the ground and excited wavefunctions of different Hamiltonians suitable for the study the vibrations of molecular systems. The generated neural networks are trained with Hamiltonians that have analytical solutions, and ask the network to generalize these solutions to more complex Hamiltonian functions. This approach allows to reproduce the excited vibrational wavefunctions of different molecular potentials. All methodologies used here are data-driven, therefore they do not assume any information about the underlying physical model of the system. This makes this approach versatile, and can be used in the study of multiple systems in quantum chemistry.