Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
This work addresses the challenge of developing force fields for excited states in computational chemistry, offering a method to improve predictions for molecular properties, though it appears incremental in advancing existing techniques.
The authors tackled the problem of characterizing complex excited state dynamics in polyatomic systems by proposing a time series clustering algorithm to identify meta-stable patterns from ab initio trajectories, enabling accurate prediction of ground and excited state properties with similar error levels, as demonstrated on sinapic acids.
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.