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

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 da

SUBMITTER: Liu F 

PROVIDER: S-EPMC5562909 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature