Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset

TL;DR

This paper applies local and global diffusion maps to evaluate collective variables and analyze reaction dynamics in a hydrogen combustion dataset, demonstrating effectiveness in identifying transition regions and assessing CV quality.

Contribution

It introduces the use of diffusion maps for analyzing quantum ab initio molecular dynamics data and assessing collective variables in hydrogen combustion reactions.

Findings

Diffusion maps effectively assess CV quality in quantum MD data.

Global diffusion maps identify transition regions in reaction channels.

The approach extends previous classical MD analysis to quantum simulations.

Abstract

We use local diffusion maps to assess the quality of two types of collective variables (CVs) for a recently published hydrogen combustion benchmark dataset~\cite{guan2022benchmark} that contains ab initio molecular dynamics trajectories and normal modes along minimum energy paths. This approach was recently advocated in~\cite{tlldiffmap20} for assessing CVs and analyzing reactions modeled by classical molecular dynamics simulations. We report the effectiveness of this approach to molecular systems modeled by quantum ab initio molecular dynamics. In addition to assessing the quality of CVs, we also use global diffusion maps to perform committor analysis as proposed in~\cite{tlldiffmap20}. We show that the committor function obtained from the global diffusion map allows us to identify transition regions of interest in several hydrogen combustion reaction channels.