Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development

TL;DR

This paper introduces a clustering method to identify meta-stable excited state patterns from ab initio dynamics, enabling accurate prediction of excited state properties and informing force field development.

Contribution

It presents a novel time series clustering approach to analyze excited state dynamics and proposes an interpolation scheme for property prediction, aiding force field construction.

Findings

Meta-stable patterns can be extracted from ab initio trajectories.

Ground and excited state properties can be predicted with similar accuracy.

Insights gained may assist in developing excited state force fields.

Abstract

The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to generate most relevant meta-stable patterns directly from ab initio dynamic trajectories. Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories. As illustrated with the example of sinapic acids, the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties with similar accuracy. These results may provide us some insights to construct an excited state force field with compatible energy terms as traditional ones.