ArticleScientific reports2017
Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Choosing the right molecular machine learning potential.Chemical science · 2021Article
- Molecular excited states through a machine learning lens.Nature reviews. Chemistry · 2021Review
- PySurf: A Framework for Database Accelerated Direct Dynamics.Journal of chemical theory and computation · 2020Article
- Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics.The journal of physical chemistry letters · 2020Article
- Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression.Frontiers in chemistry · 2020Article
- Machine learning enables long time scale molecular photodynamics simulations.Chemical science · 2019Article
- Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.BioMed research international · 2019Review
- Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database.ACS omega · 2018Article
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Authors and funding
4 authors.
Funding
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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.
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Registered trials
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