Artificial Neural Networks as Propagators in Quantum Dynamics

The utilization of artificial neural networks (ANNs) provides strategies for accelerating molecular simulations. Herein, ANNs are implemented as propagators of the time-dependent Schrodinger 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.

Automated summary

The utilization of artificial neural networks (ANNs) as propagators of the time-dependent Schrodinger equation accelerates molecular simulations, successfully applied to systems with time-dependent potentials such as proton transfer. Trained ANN propagators map wavepackets from a given time to a future time, enabling simulation over long time scales, and have the potential for diverse quantum dynamical simulations of chemical and biological processes.