Automated Transition State Searches without Evaluating the Hessian

Accurate and speedy determination of transition structures (TSs) is essential for computational studies on reaction pathways, particularly when the process involves expensive electronic structure calculations. Many search algorithms require a good initial guess of the TS geometry, as well as a Hessian input that possesses a structure consistent with the desired saddle point. Among the double-ended interpolation methods for generation of the guess for the TS, the freezing string method (FSM) is proven to be far less expensive compared to its predecessor, the growing string method (GSM). In this paper, it is demonstrated that the efficiency of this technique can be improved further by replacing the conjugate gradient optimization step (FSM-CG) with a quasi-Newton line search coupled with a BFGS Hessian update (FSM-BFGS). A second crucial factor that affects the speed with which convergence to the TS is achieved is the quality and cost of the Hessian of the energy for the guessed TS. For electronic structure calculations, the cost of calculating an exact Hessian increases more rapidly with system size than the energy and gradient. Therefore, to sidestep calculation of the exact Hessian, an approximate Hessian is constructed, using the tangent direction and local curvature at the TS guess. It is demonstrated that the partitioned-rational function optimization algorithm for locating TSs with this approximate Hessian input performs at least as well as with an exact Hessian input in most test cases. The two techniques, FSM and approximate Hessian construction, therefore can significantly reduce costs associated with finding TSs.