Constructing grids for molecular quantum dynamics using an autoencoder

Abstract

A challenge for molecular quantum dynamics (QD) calculations is the curse of dimensionality with respect to the nuclear degrees of freedom. A common approach that works especially well for fast reactive processes is to reduce the dimensionality of the system to a few most relevant coordinates. Identifying these can become a very difficult task, since they often are highly unintuitive. We present a machine learning approach that utilizes an autoencoder that is trained to find a low-dimensional representation of a set of molecular configurations. These configurations are generated by trajectory calculations performed on the reactive molecular systems of interest. The resulting low-dimensional representation can be used to generate a potential energy surface grid in the desired subspace. Using the G-matrix formalism to calculate the kinetic energy operator, QD calculations can be carried out on this grid. In addition to step-by-step instructions for the grid construction, we present the application to a test system.

24 pages, 6 figures, article

References in corpus (1)

• Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes

Cited by in corpus (5)

• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• Krotov Method for Optimal Control in Closed Quantum Systems
• GRAPE optimization for open quantum systems with time-dependent decoherence rates driven by coherent and incoherent controls
• Analysis of Trajectory Similarity and Configuration Similarity in On-the-Fly Surface-Hopping Simulation on Multi-Channel Nonadiabatic Photoisomerization Dynamics
• Enhanced photoisomerization with hybrid metallodielectric cavities based on mode interference