Subject Area
Chemistry
Abstract
With the rapid advancement of computing capabilities, computational chemistry has become increasingly indispensable in both experiment design and the interpretation of experimental outcomes. Wave-function-based quantum mechanistic methods are highly sought after for their ability to provide high-accuracy data and their potential for systematic improvement. However, to extend their applicability to larger molecules, it is essential to employ rank-reducing approximations to these methods. This dissertation dedicates Chapters Three and Four to the development of rank-reduced methodologies. Additionally, beyond single-point energies, molecular geometries and properties of molecule-excited states hold paramount importance across diverse fields of chemistry. Hence, Chapter Five presents the work on the implementation of the analytic gradient for the EOM-CCSD* method. Moreover, molecular mechanisms play an indispensable role, particularly in their scalability to biocomplexes. Chapter Six exemplifies this by demonstrating the advantageous application of molecular dynamics in exploring the protein allostery mechanism.
Degree Date
Spring 5-11-2024
Document Type
Dissertation
Degree Name
Ph.D.
Department
Chemistry
Advisor
Devin A. Matthews
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Zhao, Tingting, "ADVANCING COUPLED CLUSTER METHODS: TENSOR FACTORIZATION AND ANALYTIC GRADIENT IMPLEMENTATION" (2024). Chemistry Theses and Dissertations. 45.
https://scholar.smu.edu/hum_sci_chemistry_etds/45
