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 guid...

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Autores principales:	Fang Liu, Likai Du, Dongju Zhang, Jun Gao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ec4af1fd1903499a8a265331b5b70720
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spelling	oai:doaj.org-article:ec4af1fd1903499a8a265331b5b707202021-12-02T12:32:07ZDirect Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models10.1038/s41598-017-09347-22045-2322https://doaj.org/article/ec4af1fd1903499a8a265331b5b707202017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-09347-2https://doaj.org/toc/2045-2322Abstract 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.Fang LiuLikai DuDongju ZhangJun GaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fang Liu Likai Du Dongju Zhang Jun Gao Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
description	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.
format	article
author	Fang Liu Likai Du Dongju Zhang Jun Gao
author_facet	Fang Liu Likai Du Dongju Zhang Jun Gao
author_sort	Fang Liu
title	Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
title_short	Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
title_full	Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
title_fullStr	Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
title_full_unstemmed	Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
title_sort	direct learning hidden excited state interaction patterns from ab initio dynamics and its implication as alternative molecular mechanism models
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/ec4af1fd1903499a8a265331b5b70720
work_keys_str_mv	AT fangliu directlearninghiddenexcitedstateinteractionpatternsfromabinitiodynamicsanditsimplicationasalternativemolecularmechanismmodels AT likaidu directlearninghiddenexcitedstateinteractionpatternsfromabinitiodynamicsanditsimplicationasalternativemolecularmechanismmodels AT dongjuzhang directlearninghiddenexcitedstateinteractionpatternsfromabinitiodynamicsanditsimplicationasalternativemolecularmechanismmodels AT jungao directlearninghiddenexcitedstateinteractionpatternsfromabinitiodynamicsanditsimplicationasalternativemolecularmechanismmodels
_version_	1718394191388606464

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

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