The work in this thesis mainly focuses on the assessment of density functional methods for computing structures and energies of organic and bioorganic molecules. Previous studies found dramatic conformational and stability changes from B3LYP to MP2 geometry optimization for some Tyr-Gly conformers. Possible reasons could be large intramolecular basis set superposition errors (BSSEs) in the MP2 calculations and the lack of dispersion in the B3LYP calculations. The fragmentation method and three kinds of rotation methods were used to investigate intramolecular BSSE. It is concluded that the rotation method cannot be used to correct intramolecular BSSE along a rotation profile. Another methodology is to employ modern density functionals. We focused on M06-L with the Tyr-Gly conformer ‘book6’. Potential energy profiles were determined by computing the energy for geometries optimized at various fixed values of a distance that controls the degree of foldedness of the structure. M06-L manifested itself as a very promising method to investigate the potential energy surface of small peptides containing aromatic residues. To predict Tyr-Gly structures, 108 potential conformers were created with a Fortran program. The geometry optimizations were done using M06-L/6-31G(d) and M05-2X/6-31+G(d). Two schemes were employed and the most stable conformers were compared to the 20 stable conformers found by B3LYP. Both schemes found 10 conformers similar to one of the B3LYP stable conformers, as well as several newly found conformers. The study of a missing B3LYP stable conformer showed that the possible reason of missing conformers may be the lack in dispersion in B3LYP theory. To study the hydration effect, we studied the conformations of neutral and zwitterionic 3-fluoro-γ-aminobutyric acid (3F-GABA) in solution using different solvation models, mainly the explicit water molecule models. Zwitterionic forms of 3F-GABA are preferred in solution. M06-2X performs better in calculating transition energy profiles than MP2.

L’objectif principal de cette thèse était de comprendre par une approche conjointe théorie/expérience des réactions de polymérisation de monomères polaires, plus particulièrement de dérivés (méth)acryliques, réactions organocatalysées par des carbènes N-Hétérocycliques (NHC) utilisés seuls ou associés à une molécule organique (alcool) ou à un acide de Lewis. Pour cela un travail de rationalisation des effets électroniques et stériques apportés par le carbène a été réalisé à la fois sur les premières étapes du mécanisme de polymérisation par la théorie de la fonctionnelle de la densité (DFT) mais également sur les mécanismes potentiellement concurrents (cyclo-dimérisation) par deux méthodes théoriques: la DFT et la dynamique moléculaire (MD). L’ajout d’une molécule tierce comme un alcool ou un acide de Lewis (borane ou silane) a également été étudié théoriquement en détail afin de mieux comprendre l’effet de cette association sur la nature du mécanisme privilégié et sur l’efficacité de la polymérisation. / The main objective of this thesis was to understand, by a joint theoretical/experimental approach, polymerization reactions of polar monomers, especially (meth)acrylic derivatives, organocacatalyzed polymerizations by N-Heterocyclic carbenes (NHC) used alone or in combination with an organic molecule (alcohol) or a Lewis acid. Rationalization of both electronic and steric effects of the carbene was performed on the first steps of the polymerization mechanism by the functional density theory (DFT) and also on potentially competitive mechanisms (cyclodimerization) by two theoretical methods: DFT and molecular dynamics (MD). The addition of a third molecule such as an alcohol or a Lewis acid (borane or silane) has also been theoretically studied in detail in order to better understand the effect of this association on the nature of the privileged pathway and the effectiveness of the polymerization.

<p>In this thesis, we present novel computational methods and frameworks to address the challenges associated with the determination of free energy profiles for condensed-phase chemical reactions using combined quantum mechanical and molecular mechanical (QM/MM) approaches. We focus on overcoming issues related to force matching, molecular polarizability, and convergence of free energy profiles. First, we introduce a method called Reaction Path-Force Matching in Collective Variables (RP-FM-CV) that efficiently carries out ab initio QM/MM free energy simulations through mean force fitting. This method provides accurate and robust simulations of solution-phase chemical reactions by significantly reducing deviations on the collective variables forces, thereby bringing simulated free energy profiles closer to experimental and benchmark AI/MM results. Second, we explore the role of pairwise repulsive correcting potentials in generating converged free energy profiles for chemical reactions using QM/MM simulations. We develop a free energy correcting model that sheds light on the behavior of repulsive pairwise potentials with large force deviations in collective variables. Our findings contribute to a deeper understanding of force matching models, paving the way for more accurate predictions of free energy profiles in chemical reactions. Next, we address the underpolarization problem in semiempirical (SE) molecular orbital methods by introducing a hybrid framework called doubly polarized QM/MM (dp-QM/MM). This framework improves the response property of SE/MM methods through high-level molecular polarizability fitting using machine learning (ML)-derived corrective polarizabilities, referred to as chaperone polarizabilities. We demonstrate the effectiveness of the dp-QM/MM method in simulating the Menshutkin reaction in water, showing that ML chaperones significantly reduce the error in solute molecular polarizability, bringing simulated free energy profiles closer to experimental results. In summary, this thesis presents a series of novel methods and frameworks that improve the accuracy and reliability of free energy profile estimations in condensed-phase chemical reactions using QM/MM simulations. By addressing the challenges of force matching, molecular polarizability, and convergence, these advancements have the potential to impact various fields, including computational chemistry, materials science, and drug design.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / In this thesis, we present novel computational methods and frameworks to address the challenges associated with the determination of free energy profiles for condensed-phase chemical reactions using combined quantum mechanical and molecular mechanical (QM/MM) approaches. We focus on overcoming issues related to force matching, molecular polarizability, and convergence of free energy profiles. First, we introduce a method called Reaction Path-Force Matching in Collective Variables (RP-FM-CV) that efficiently carries out ab initio QM/MM free energy simulations through mean force fitting. This method provides accurate and robust simulations of solution-phase chemical reactions by significantly reducing deviations on the collective variables forces, thereby bringing simulated free energy profiles closer to experimental and benchmark AI/MM results. Second, we explore the role of pairwise repulsive correcting potentials in generating converged free energy profiles for chemical reactions using QM/MM simulations. We develop a free energy correcting model that sheds light on the behavior of repulsive pairwise potentials with large force deviations in collective variables. Our findings contribute to a deeper understanding of force matching models, paving the way for more accurate predictions of free energy profiles in chemical reactions. Next, we address the underpolarization problem in semiempirical (SE) molecular orbital methods by introducing a hybrid framework called doubly polarized QM/MM (dp-QM/MM). This framework improves the response property of SE/MM methods through high-level molecular polarizability fitting using machine learning (ML)-derived corrective polarizabilities, referred to as chaperone polarizabilities. We demonstrate the effectiveness of the dp-QM/MM method in simulating the Menshutkin reaction in water, showing that ML chaperones significantly reduce the error in solute molecular polarizability, bringing simulated free energy profiles closer to experimental results. In summary, this thesis presents a series of novel methods and frameworks that improve the accuracy and reliability of free energy profile estimations in condensed-phase chemical reactions using QM/MM simulations. By addressing the challenges of force matching, molecular polarizability, and convergence, these advancements have the potential to impact various fields, including computational chemistry, materials science, and drug design.

This thesis examines the energy efficiency of Advanced Encryption Standard (AES) encryption across various modes of operation (ECB, CBC, CFB, OFB, CTR, GCM, and CCM) on ARM Cortex-A53, Cortex-A72, and Cortex-A76 processors, using Raspberry Pi models 3, 4, and 5 as the experimental platforms. The study primarily investigates the impact of key lengths (128, 192, and 256 bits) and data sizes on energy consumption during encryption tasks. Using an experimental setup with the Raspberry Pi single-board computers, energy consumption was measured and analyzed through repeated encryption operations and data collection via a power meter interfaced with a database. The results reveal only modest increases in energy consumption with larger key lengths across all tested modes and data sizes, suggesting that while key length incrementally affects energy usage, the impact remains relatively minor, thus not significantly compromising energy efficiency for enhanced security. The analysis further shows that ECB mode consistently exhibits the lowest energy consumption, with CTR and CBC not far behind, followed by OFB and then CFB being the least effective among the traditional modes, with AEAD modes like GCM and CCM demanding substantially higher energy, reflecting their more complex processing requirements. Additionally, the study highlights the influence of data size on energy efficiency, showing a decrease in energy consumption per kilobyte with increasing file size up to a certain point, beyond which the benefits diminish. This thesis contributes to a deeper understanding of the trade-offs between security features and energy efficiency in AES encryption on ARM processors, offering insights into scenarios where energy consumption is a critical concern. The findings underscore the importance of selecting appropriate encryption modes and configurations based on the specific requirements and constraints of hardware environments aimed at optimizing energy efficiency in cryptographic operations. Future research could expand on a broader array of ARM-based devices to improve the biases from the Raspberry Pi boards and enhance the reliability of the conclusions drawn from the data.