DFT Studies of the Organocatalytic Aldol Reaction and a Frustrated Lewis Pair

The computational study, and in particular the density functional theory (DFT) study of the organocatalytic α-chlorination-aldol reaction and the chiral backbone Frustrated Lewis Pair (FLP) system served as a valuable tool for experimental purposes. This thesis describes methods to consider different transition states of the proline- catalyzed α-chlorination aldol reaction to determine the reasonable transition state in the reaction between the enamine and α-chloro aldehydes. Moreover, the novel intramolecular Frustrated Lewis pair based on a chiral backbone for the asymmetric hydrogenation of imines and enamines was designed and the ability of hydrogen splitting by this new FLP system was examined by computational modeling and calculating the hydrogen activation energy barrier.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

Forecasting the Yield Curve of Government Bonds: A Comparative Study

For the past 20 years, researchers have applied the Kalman filter to the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model based on Diebold and Li (2006) and Nelson and Siegel’s (1987) three-factor model, and estimate this dynamic model using the Kalman filter. We compare both in-sample and out-of-sample performance of our dynamic methods with various other models in the literature. We find that our dynamic model dominates existing models in medium- and long-horizon yield curve predictions. However, the dynamic model should be used with caution when forecasting short maturity yields