Literature DB >> 34879677

Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface.

Shichen Lin1, Daoling Peng1, Weitao Yang2, Feng Long Gu1, Zhenggang Lan1.   

Abstract

The H-atom dissociation of formaldehyde on the lowest triplet state (T1) is studied by quasi-classical molecular dynamic simulations on the high-dimensional machine-learning potential energy surface (PES) model. An atomic-energy based deep-learning neural network (NN) is used to represent the PES function, and the weighted atom-centered symmetry functions are employed as inputs of the NN model to satisfy the translational, rotational, and permutational symmetries, and to capture the geometry features of each atom and its individual chemical environment. Several standard technical tricks are used in the construction of NN-PES, which includes the application of clustering algorithm in the formation of the training dataset, the examination of the reliability of the NN-PES model by different fitted NN models, and the detection of the out-of-confidence region by the confidence interval of the training dataset. The accuracy of the full-dimensional NN-PES model is examined by two benchmark calculations with respect to ab initio data. Both the NN and electronic-structure calculations give a similar H-atom dissociation reaction pathway on the T1 state in the intrinsic reaction coordinate analysis. The small-scaled trial dynamics simulations based on NN-PES and ab initio PES give highly consistent results. After confirming the accuracy of the NN-PES, a large number of trajectories are calculated in the quasi-classical dynamics, which allows us to get a better understanding of the T1-driven H-atom dissociation dynamics efficiently. Particularly, the dynamics simulations from different initial conditions can be easily simulated with a rather low computational cost. The influence of the mode-specific vibrational excitations on the H-atom dissociation dynamics driven by the T1 state is explored. The results show that the vibrational excitations on symmetric C-H stretching, asymmetric C-H stretching, and C=O stretching motions always enhance the H-atom dissociation probability obviously.

Entities:  

Year:  2021        PMID: 34879677      PMCID: PMC8654486          DOI: 10.1063/5.0067176

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  69 in total

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Authors:  Oded Godsi; Michael A Collins; Uri Peskin
Journal:  J Chem Phys       Date:  2010-03-28       Impact factor: 3.488

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Authors:  H M Yin; S H Kable; X Zhang; J M Bowman
Journal:  Science       Date:  2006-03-10       Impact factor: 47.728

3.  Photodissociation dynamics of formaldehyde initiated at the T1/S0 minimum energy crossing configurations.

Authors:  Benjamin C Shepler; Evgeny Epifanovsky; Peng Zhang; Joel M Bowman; Anna I Krylov; Keiji Morokuma
Journal:  J Phys Chem A       Date:  2008-12-25       Impact factor: 2.781

Review 4.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

5.  Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks.

Authors:  Yaolong Zhang; Xueyao Zhou; Bin Jiang
Journal:  J Phys Chem Lett       Date:  2019-03-01       Impact factor: 6.475

6.  Application of Clustering Algorithms to Partitioning Configuration Space in Fitting Reactive Potential Energy Surfaces.

Authors:  Yafu Guan; Shuo Yang; Dong H Zhang
Journal:  J Phys Chem A       Date:  2018-03-15       Impact factor: 2.781

7.  Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy.

Authors:  Qidong Lin; Yaolong Zhang; Bin Zhao; Bin Jiang
Journal:  J Chem Phys       Date:  2020-04-21       Impact factor: 3.488

8.  Global analytical potential energy surface for the electronic ground state of NH3 from high level ab initio calculations.

Authors:  Roberto Marquardt; Kenneth Sagui; Jingjing Zheng; Walter Thiel; David Luckhaus; Sergey Yurchenko; Fabio Mariotti; Martin Quack
Journal:  J Phys Chem A       Date:  2013-08-01       Impact factor: 2.781

9.  Atomic structure of boron resolved using machine learning and global sampling.

Authors:  Si-Da Huang; Cheng Shang; Pei-Lin Kang; Zhi-Pan Liu
Journal:  Chem Sci       Date:  2018-09-11       Impact factor: 9.825

10.  Machine learning enables long time scale molecular photodynamics simulations.

Authors:  Julia Westermayr; Michael Gastegger; Maximilian F S J Menger; Sebastian Mai; Leticia González; Philipp Marquetand
Journal:  Chem Sci       Date:  2019-08-05       Impact factor: 9.969

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  1 in total

Review 1.  Reaction Space Projector (ReSPer) for Visualizing Dynamic Reaction Routes Based on Reduced-Dimension Space.

Authors:  Takuro Tsutsumi; Yuriko Ono; Tetsuya Taketsugu
Journal:  Top Curr Chem (Cham)       Date:  2022-03-10
  1 in total

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