Abstract
Chemical catalysis is one of the most important applications in chemistry and has been developed for years, using both experimental techniques and computational methods.
The first part of my dissertation focuses on mechanistic studies of various types of catalysts based on quantum chemical calculations. In my work, I investigated [NiFe] hydrogenase, [NHC]Au(I)-catalysts, and actinide sandwich complexes, by applying theoretical tools ranging from vibrational spectroscopy to all-electron relativistic methodologies. New comprehensive insights into the reaction mechanisms were obtained, forming the basis for the design of the next generation of catalysts.
An additional aim of my research included the development of innovative methods for computational chemistry. In the second part, I will present a new machine learning-based method that provides transition state geometries. Also, I will show how the machine learning nano-reactor produces a variety of organic molecules by analyzing the reaction networks.
Degree Date
Spring 5-15-2021
Document Type
Dissertation
Degree Name
Ph.D.
Department
Chemistry
Advisor
Elfi Kraka
Subject Area
Chemistry
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Makos, Malgorzata, "Mechanistic Studies of Catalysis Through Quantum Chemical and Machine Learning Approaches" (2021). Chemistry Theses and Dissertations. 21.
https://scholar.smu.edu/hum_sci_chemistry_etds/21
