研究目的
To develop a method to obtain approximate geometries for minimum energy conical intersections (MECIs) between the ground and first excited singlet electronic states using time-dependent density functional theory (TDDFT) and to validate it against multireference theories.
研究成果
The proposed ES/TDDFT approach effectively provides approximate S0/S1-MECI geometries that are in good agreement with those from multireference theories. It overcomes discontinuities in TDDFT potential energy surfaces and, when combined with automated search methods, can systematically explore MECIs. This method is promising for studying photoreaction mechanisms in medium-sized molecules.
研究不足
The method relies on TDDFT, which may have inherent limitations in describing excited states accurately compared to multireference methods. The energy shift method introduces an approximation, and the approach is tested only on benzene and naphthalene, so its generalizability to other molecules is not fully established. Computational costs and the need for specific software (e.g., Gaussian09, MOLPRO2012, GRRM) may limit accessibility.
1：Experimental Design and Method Selection:
The study uses the energy shift (ES) method with TDDFT to avoid discontinuities in potential energy surfaces around conical intersections. The gradient projection (GP) method is employed for MECI optimization, and the branching plane update (BPU) approach is used to estimate the branching plane.
2：Sample Selection and Data Sources:
Benzene and naphthalene are used as benchmark molecules, with initial geometries taken from previous studies using CASPT2 and SF-TDDFT methods.
3：List of Experimental Equipment and Materials:
Computational methods include TDDFT with BHandHLYP and ωB97X-D functionals, CASSCF, CASPT2, and SF-TDDFT. Basis sets such as 6-31G* and cc-pVDZ are used.
4：Experimental Procedures and Operational Workflow:
Approximate S0/S1-MECIs are optimized using the ES method with various shift values (ε = 5, 10, 30, 100 kJ/mol). Geometry similarity is judged using root mean square deviation (RMSD) and maximum difference criteria. Automated searches are performed using the GP/SC-AFIR method.
5：Data Analysis Methods:
Energies and geometries are compared across computational levels. Statistical measures like RMSD and tmax are used for geometry comparison, and energy differences are analyzed relative to ground state minima.
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