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Made available in DSpace on 2014-10-09T12:23:14Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:58:13Z (GMT). No. of bitstreams: 1 00792.pdf: 2829266 bytes, checksum: 4d674b11215a8b74c9241b56eaec06e1 (MD5) / Tese (Livre - Docencia) / IEA/T / Faculdade de Ciencias Farmaceuticas, Universidade de Sao Paulo - CF/USP

Geometry optimization is a fundamental step in the numerical modelling of chemical reactions. Many thermodynamic and kinetic properties are closely related to the structure of the reactant, product, and the transition states connecting them. Different from the reaction and product, which are local minima on the potential energy surface, a transition state is the first-order saddle point with only one negative curvature. Over years, many methods have been devised to tackle the problem. Locating stable structures is relatively easy with a reliable algorithm and high accuracy. One can follow the gradient descent direction to pursuit the local minimum until convergence is reached. But for the transition state, the determination is more challenging as either the up-hill or down-hill direction is allowed in the process. Motivated by the difficulty, many well-designed optimization algorithms are elaborated specifically to stress the problem. The performance of geometry optimization is affected by various aspects: the initial guess structure, the coordinate system representing the molecule, the accuracy of the initial Hessian matrix, the Hessian update schemes, and the step-size control of each iteration. In this thesis, we propose a new geometry optimization algorithm considering all the important components. More specifically, in Chapter 2, a new set of robust dihedral and redundant internal coordinates is introduced to effectively represent the molecular structures, as well as a computational efficient transformation method to generate a guess structure. In Chapter 3 and 5, a sophisticated robust algorithm is presented and tested to solve intricate transition state optimization problems. In Chapter 4, a new algorithm to exploring reaction pathways based on redundant internal coordinates is illustrated with real chemical reactions. Last but not least, in Chapter 6, a systematic test to explore the optimal methods in each procedure is presented. A well-performed combination of optimization methods is drawn for generic optimization purposes. All the methods and algorithms introduced in this thesis is included in our forth-coming open-source Python package named GOpt. It's a general-purpose library that can work in conjunction with major quantum chemistry software including Gaussian. More features are under development and await to be released in the coming update. / Thesis / Doctor of Science (PhD)

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Call number: LD2668 .T4 1960 C68

This research has been directed at the study of neighboring group participation in electrooxidation of thioethers. Controlled potential oxidation of substituted 1,3-dithiane in wet acetonitrile provides substituted 1,2-dithiolane 1-oxide in good yield. Thioethers appended with neighboring alcohols and carboxylate are catalytically oxidized in a redox cycle by bromide ion. The formation of the alkoxysulfonium salt intermediates in such reactions is confirmed by product study. On the other hand, the acyloxysulfonium salt intermediates in the electrooxidation of endo -6-methylthio-bicyclo [2.2.1] heptane-2- endo -carboxylic acid (1) are unstable at room temperature. Control experiments using ¹⁸O labeled compounds prove unequivocally the existent of the acyloxysulfonium salt intermediates. Diastereospecific oxidation of 1 and its methyl ester with DABCO.2Br₂ complex and m-CPBA is sterically controlled. In both cases, similar product ratios are observed which is explained by the participation of the carboxylic acid group in the case of DABCO.2Br₂ oxidation but not in the case of m-CPBA oxidation. The structure of one of the diastereomer of the endo acid sulfoxide is unequivocally proved by x-ray crystallographic analysis.

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published_or_final_version / Chemistry / Doctoral / Doctor of Philosophy