| Summary: | Computational chemistry greatly enhances the scientific analysis of experiments and can predict quantities that are experimentally not accessible. However, there is still no approximate method available that is both efficient and accurate enough to serve as general basis for large-scale applications in computational chemistry, at least in the range of 20 to 100 atoms, which covers many challenging problems of present day chemical research. In this dissertation, an accurate and efficient approximation to single reference correlation methods was developed which constitutes a new family of local correlation methods denoted local pair natural orbital (LPNO) methods. The LPNO methods achieve efficiency through localization of the internal space and truncation of the electron pair list together with a tremendous compression of the external space using a truncated pair natural orbitals expansion. Using the LPNO approach, serial and parallel production level implementations for various coupled pair methods, quadratic configuration interaction and coupled cluster with single and double excitations were developed in the framework of the ORCA quantum chemistry program package. Only three cut-off parameters enter the procedure, which control the number of PNOs per electron pair, the size of the significant electron pair list, and the number of contributing auxiliary basis functions per PNO. The rather conservatively chosen default values for the thresholds do not need to be changed or reinvestigated in detail prior to any application study and the LPNO methods can be used in the same way as their untruncated counterparts. The laborious integral transformation associated with the large number of PNOs becomes feasible through the extensive use of local density fitting (RI) techniques. The LPNO approach offers a number of attractive features: a) the smooth and controllable truncation errors; b) the excellent behavior with respect to basis set extension; c) the very compact form of the LPNO wavefunction; d) the absence of any ...
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