EOM-CCSD-Based Neural Network Diabatic Potential Energy Matrix for ^(1)πσ^(*)-Mediated Photodissociation of Thiophenol

A new diabatic potential energy matrix(PEM)of the coupled~^(1)ππ^(*)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 me V,respectively,suggesting the high accuracy of the NN method as expected.The calculated lifetimes of the S1 vibronic 00 and31 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.

Three-Dimensional Diabatic Potential Energy Surfaces of Thiophenol withNeural Networks

Three-dimensional(3D)diabatic potential energy surfaces(PESs)of thiophenol involving the S0,and coupled 1ππ^(*) and 1πσ^(*) states were constructed by a neural network approach.Specifically,the diabatization of the PESs for the 1ππ^(*) and 1πσ^(*) states was achieved by the fitting approach with neural networks,which was merely based on adiabatic energies but with the correct symmetry constraint on the off-diagonal term in the diabatic potential energy matrix.The root mean square errors(RMSEs)of the neural network fitting for all three states were found to be quite small(<4 meV),which suggests the high accuracy of the neural network method.The computed low-lying energy levels of the S_(0) state and lifetime of the 0^(0) state of S_(1) on the neural network PESs are found to be in good agreement with those from the earlier diabatic PESs,which validates the accuracy and reliability of the PESs fitted by the neural network approach.