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oai:null:10339/374272026-09-02T12:42:56Zcom_10339_14934col_10339_38132

Kolb, Brian 2012-09-05T08:35:15Z 2014-09-05T08:30:09Z 2012 https://wakespace.lib.wfu.edu/handle/10339/37427 van der Waals interactions are weak, non-specific forces arising at the atomic scale. Although they are weak---much weaker than a covalent or ionic interaction---they occur in large numbers, and in total, can make a significant contribution to the properties of a system. Several systems are herein explored whose properties are influenced strongly by van der Waals interactions. These systems are investigated largely through the non-local van der Waals density functional (vdW-DF) working within density functional theory (DFT), with accurate quantum chemistry calculations and experimental results to serve as a reference against which we compare our results. Properties calculated for (H<sub>2</sub>O)<sub>n</sub> with n=1--5 showed systematic improvement when van der Waals interactions were included. The low-temperature phase of Mg(BH<sub>4</sub>)<sub>2</sub> is incorrectly predicted by standard local or semi-local approximations. However, the inclusion of van der Waals interactions brings theory in line with experiment. Dimers of phenalenyl and the nitrogen- and boron-substituted closed-shell analogues show an interesting collection of phenomena, including a 2-electron/multi-center bond and an anomalous barrier in a rotational total energy profile caused by electron kinetic energy. In the final part of this work, the theoretical groundwork is laid for a computational tool that uses network concepts to perform analytical calculations. These \emph{network functions} are capable of learning the mathematical connection in a set of data. A course to use network functions to improve DFT through a search for a kinetic energy functional and an improved exchange-correlation functional is discussed. en Wake Forest University chemical physics density functional theory machine learning van der Waals An ab Initio Investigation of van der Waals-Rich Systems and a Machine Learning Approach to Finding Analytical Functions Describing Tabulated Data Dissertation Thonhauser, Timo Thonhauser, Timo Salam, Akbar Cook, Gregory B Holzwarth, Natalie Jurchescu, Oana Physics 2014-09-05 open.access