Artificial Neural Networks as Propagators in Quantum Dynamics

Maxim Secor, Alexander V. Soudackov, Sharon Hammes-Schiffer

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The utilization of artificial neural networks (ANNs) provides strategies for accelerating molecular simulations. Herein, ANNs are implemented as propagators of the time-dependent Schrödinger equation to simulate the quantum dynamics of systems with time-dependent potentials. These ANN propagators are trained to map nonstationary wavepackets from a given time to a future time within the discrete variable representation. Each propagator is trained for a specified time step, and iterative application of the propagator enables the propagation of wavepackets over long time scales. Such ANN propagators are developed and applied to one- and two-dimensional proton transfer systems, which exhibit nuclear quantum effects such as hydrogen tunneling. These ANN propagators are trained for either a specific time-independent potential or general potentials that can be time-dependent. Hierarchical, multiple time step algorithms enable parallelization, and the extension to higher dimensions is straightforward. This strategy is applicable to quantum dynamical simulations of diverse chemical and biological processes.

Original languageEnglish (US)
Pages (from-to)10654-10662
Number of pages9
JournalJournal of Physical Chemistry Letters
Volume12
Issue number43
DOIs
StatePublished - Nov 4 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Physical and Theoretical Chemistry

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