EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for <inline-formula><tex-math id="M1">\begin{document}$ ^\textbf{1} $\end{document}</tex-math></inline-formula><inline-formula><tex-math id="M2">\begin{document}$ \mathit{\boldsymbol{\pi}} $\end{document}</tex-math></inline-formula><inline-formula><tex-math id="M3">\begin{document}$ \mathit{\boldsymbol{\sigma^*}} $\end{document}</tex-math></inline-formula>-Mediated Photodissociation of Thiophenol

Siting Hou; Chaofan Li; Huixian Han; Changjian Xie

doi:10.1063/1674-0068/cjcp2201016

Volume 35 Issue 3

Jun. 2022

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Article Navigation > Chinese Journal of Chemical Physics > 2022 > 35(3): 461-470

Siting Hou, Chaofan Li, Huixian Han, Changjian Xie. EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for $ ^\textbf{1} $$ \mathit{\boldsymbol{\pi}} $$ \mathit{\boldsymbol{\sigma^*}} $-Mediated Photodissociation of Thiophenol[J]. Chinese Journal of Chemical Physics , 2022, 35(3): 461-470. doi: 10.1063/1674-0068/cjcp2201016

Citation:

Siting Hou, Chaofan Li, Huixian Han, Changjian Xie. EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for $ ^\textbf{1} $$ \mathit{\boldsymbol{\pi}} $$ \mathit{\boldsymbol{\sigma^*}} $-Mediated Photodissociation of Thiophenol[J]. Chinese Journal of Chemical Physics , 2022, 35(3): 461-470. doi: 10.1063/1674-0068/cjcp2201016

Citation:

Siting Hou, Chaofan Li, Huixian Han, Changjian Xie. EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for $ ^\textbf{1} $$ \mathit{\boldsymbol{\pi}} $$ \mathit{\boldsymbol{\sigma^*}} $-Mediated Photodissociation of Thiophenol[J]. Chinese Journal of Chemical Physics , 2022, 35(3): 461-470. doi: 10.1063/1674-0068/cjcp2201016

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EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for $ ^\textbf{1} $$ \mathit{\boldsymbol{\pi}} $$ \mathit{\boldsymbol{\sigma^*}} $-Mediated Photodissociation of Thiophenol

doi: 10.1063/1674-0068/cjcp2201016

a.
Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an 710127, China
b.
School of Physics, Northwest University, Xi'an 710127, China

More Information

Corresponding author: Huixian Han, E-mail: hxhan@nwu.edu.cn; Changjian Xie, E-mail: chjxie@nwu.edu.cn
Received Date: 2022-01-20
Accepted Date: 2022-02-12
Publish Date: 2022-06-27

Abstract

Abstract

A new diabatic potential energy matrix (PEM) of the coupled $ ^1 $$ \pi\pi^* $ and $ ^1\pi\sigma^* $ states for the $ ^1\pi\sigma^* $-mediated photodissociation of thiophenol was constructed using a neural network (NN) approach. The diabatization of the PEM was specifically achieved by our recent method [Chin. J. Chem. Phys. 34 , 825 (2021)], which was based on adiabatic energies without the associated costly derivative couplings. The equation of motion coupled cluster with single and double excitations (EOM-CCSD) method was employed to compute adiabatic energies of two excited states in this work due to its high accuracy, simplicity, and efficiency. The PEM includes three dimensionalities, namely the S-H stretch, C-S-H bend, and C-C-S-H torsional coordinates. The root mean square errors of the NN fitting for the S$ _1 $ and S$ _2 $ states are 0.89 and 1.33 meV, respectively, suggesting the high accuracy of the NN method as expected. The calculated lifetimes of the S$ _1 $ vibronic 0$ ^0 $ and 3$ ^1 $ states are found to be in reasonably good agreement with available theoretical and experimental results, which validates the new EOM-CCSD-based PEM fitted by the NN approach. The combination of the diabatization scheme solely based on the adiabatic energies and the use of EOM-CCSD method makes the construction of reliable diabatic PEM quite simple and efficient.
- Neural network,
- Diabatic potential energy matrix,
- Photodissociation dynamics

^†Part of Special Topic "Quantum and Classical Dynamics in Chemistry" in the 32nd Chinese Chemical Society Congress.

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References(51)

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