EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for ^\textbf1 \mathit\boldsymbol\pi \mathit\boldsymbol\sigma^* -Mediated Photodissociation of Thiophenol

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.