EOM-CCSD-based neural network diabatic potential energy matrix for 1πσ*-mediated photodissociation of thiophenol

Abstract

A new diabatic potential energy matrix (PEM) of the coupled 1ππ* and 1πσ* states for the 1πσ*-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 S1 and S2 states are 0.89 and 1.33 meV, respectively, suggesting the high accuracy of the NN method as expected. The calculated lifetimes of the S1 vibronic 00 and 31 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.