Geometry Optimization and Modeling of Complex Molecules

Abstract

The mysteries in chemistry could be reveals by utilization of innovative computational methods and the creative application of advanced modeling techniques. The limitation or difficulties of experimental methods sometimes makes it hard to study the mechanism behind the reaction energy, so computational chemistry could play an important role to investigate and analyze the detailed reaction mechanism and elementary reaction pathways behind a complexed, multi-step chemical reaction. Also, it is important to develop robust and accurate theoretical methods to perform computational and simulation works. We applied multiple computational or simulation models to study various chemical systems, which provided us valuable insights to understand the chemical reactions happened in our daily life. Chapter 1 explores an advanced Gaussian Process (GP)-based optimization approach for the efficient geometry optimization of polypeptides, focusing on reducing computational costs associated with single-point energy (SPE) evaluations in traditional methods. By employing Gaussian Process Regression (GPR) as a surrogate model, the optimization steps are minimized through a surrogate potential energy surface (PES) generated from quantum mechanical data. The study assesses the performance of four kernel types—Matern, squared exponential, rational quadratic, and periodic—within multiple coordinate frameworks, including redundant and non-redundant internal coordinates and Coulombic coordinates. Results indicate that the periodic kernel combined with non-redundant delocalized internal coordinates is the most effective in reducing optimization steps, particularly suited to handle molecular structures with periodic characteristics. Additionally, the rational quadratic kernel shows promise when used with Coulombic coordinates, offering flexibility for functions with varying smoothness. Implemented in the mad-GP framework, this study provides insights into optimizing large biomolecules, such as polypeptides, with significant implications for computational chemistry and biomolecular modeling. We also compared the GP-optimized structures with the AlphaFold-predicted structures to assess their respective effectiveness in accurate structure prediction. This comparison provides insight into the reliability and applicability of each method for modeling polypeptide conformations. Chapter 2 investigates the interaction energies and energy decomposition of van der Waals (vdW) complexes between N6-methyladenosine (m6A) and tryptophan residues in YTH proteins, the readers of m6A modifications on mRNA. Given the role of m6A in cellular processes, structural insights into its interaction with YTH proteins could facilitate therapeutic advancements. We examined the effects of various chemical modifications on tryptophan residues (W465 and W470) in the YTH binding pocket, with the aim of enhancing the CH-π interactions with m6A through modified electron density. Using Density Functional Theory (DFT) and Symmetry-Adapted Perturbation Theory (SAPT), we explored the vdW interactions into electrostatic, dispersion, induction, and Pauli exchange components and identified London dispersion and electrostatics as dominant stabilizing forces. Correlations of these components with molecular descriptors such as polarizability and multipole moments further highlighted the effects of electronic properties on binding. Our results suggest that optimized tryptophan modifications could strengthen m6A recognition, potentially guiding the design of enhanced m6A-binding proteins for applications in RNA biology. Chapter 3 presents a computational analysis of reaction pathways and energy barriers on LiCoO₂ and TiO₂ surface models, exploring their role in promoting reactions critical to lithium-ion battery (LIB) performance and catalytic applications. For LiCoO₂, we examine the dissociation of H₂O₂. Using density functional theory (DFT) and climbing-image nudged elastic band (CI-NEB) calculations, we identified and characterized the elementary steps in the dissociation mechanism, and indicated that the reaction barriers are reduced in the presence of organic species. For TiO₂, we model the adsorption and dissociation of a Li(DME)₃ complex, exploring solvent dissociation and solvent exchange mechanisms in the context of DME ligands. Results show that the TiO₂ surface aids in stabilizing Li⁺ ions after solvent dissociation, and it favors a solvent-exchange pathway with a lower reaction barrier. These insights provide valuable mechanistic detail that help the design of materials.