Energy-Represented Direct Inversion in the Iterative Subspace within a Hybrid Geometry Optimization Method

A geometry optimization method using an energy-represented direct inversion in the iterative subspace algorithm, GEDIIS, is introduced and compared with another DIIS formulation (controlled GDIIS) and the quasi-Newton rational function optimization (RFO) method. A hybrid technique that uses different methods at various stages of convergence is presented. A set of test molecules is optimized using the hybrid, GEDIIS, controlled GDIIS, and RFO methods. The hybrid method presented in this paper results in smooth, well-behaved optimization processes. The optimization speed is the fastest among the methods considered.