Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models

Fang Liu,Jun Gao,Dongju Zhang,Likai Du

doi:10.1038/s41598-017-09347-2

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, namely, the prediction with ensemble models (PEM). As illustrated with the example of sinapic acids, The PEM method does not require any training data beyond the clustering algorithm, and 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 molecular mechanism models with compatible energy terms as traditional force fields.

Highlights

The photophysical or photochemical processes are extremely important for the evolution of life and environments
In order to overcome the defects of both options, we find a trade-off between the efficiency and reliable, for which a small set of the samples (i.e. n = 10) in each pattern is used for prediction with ensemble models (PEM) algorithm, namely “mini-batch” option
Our results strongly suggest that ensemble models together with a proper classifier for model selection provides a useful research tool to gain insights from time series of ab initio dynamics, as demonstrated in the excited state studies of the sinapic acid (SA) molecule

Summary

State Interaction Patterns from ab initio Dynamics and Its Implication

Received: 20 June 2017 Accepted: 25 July 2017 Published: xx xx xxxx as Alternative Molecular Mechanism Models. As illustrated with the example of sinapic acids, The PEM method does not require any training data beyond the clustering algorithm, and 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 molecular mechanism models with compatible energy terms as traditional force fields. On the basis of these finite meta-stable patterns, the conformation similarity was explored to build an interpolation scheme, namely, the prediction with ensemble models (PEM), to estimate the ground and excited state properties of the entire dynamics trajectories. This work highlights the potential power of ML algorithm in computational chemistry to extract chemical insights or develop the state-of-art theoretical models

Results

Conclusions

Models and Methods

Additional Information