Deep learning methods for the computation of vibrational wavefunctions

摘要

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 of 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. Since the Hamiltonians solutions used to train the neural networks are computationally cheap, the training process is fast and efficient. 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. (c) 2021 Elsevier B.V. All rights reserved.