京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21123号 / 工博第4487号 / 新制||工||1697(附属図書館) / 京都大学大学院工学研究科分子工学専攻 / (主査)教授 佐藤 啓文, 教授 関 修平, 教授 山本 量一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

This dissertation describes the implementation of the molecular electronic structure calculations with an implicit solvent model using coupled-cluster theory. The theory for and the implementation of the solvent reaction field method (SCRF) and the reference interaction site model (RISM) at the coupled-cluster singles and doubles (CCSD) are presented. In the SCRF model a solute molecule is placed in a spherical cavity, and the outer solvent is represented by a dielectric continuum, which is characterized by the dielectric constant of the solvent. The reaction field is introduced to the system by using the multipole moment expansion of the electronic structure of the solute molecule and the dielectric constant. The SCRF method has been used to calculate the conformational equilibrium and the rotational barriers of 1,2-dichloroethane in vacuum and in different solvents. The calculated results are compared with experimental values. In addition, the solvent effects on the energetics of the mechanism of nitration of benzene are reported using the implemented CCSD-SCRF model. The idea of RISM is to replace the reaction field in continuum models by a microscopic expression in terms of the site-site radial distribution functions between solute and solvent, which can be calculated from the RISM integral equations. The statistical solvent distribution around the solute is determined based on the electronic structure of the solute, while the electronic structure of solute is influenced by the surrounding solvent distribution. Therefore, the wave function and the RISM equations are solved self-consistently with CCSD. Pair correlation functions, partial atomic charges, and solvation free energies of water and N-methylacetamide are calculated in liquid water using proposed theory. Both the CC-SCRF and CC-RISM methods have been implemented in a developmental version of the Q-Chem 3.2 quantum chemistry package.

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).