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An ab Initio Investigation of van der Waals-Rich Systems and a Machine Learning Approach to Finding Analytical Functions Describing Tabulated Data

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abstract
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 (H2O)n with n=1--5 showed systematic improvement when van der Waals interactions were included. The low-temperature phase of Mg(BH4)2 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.
subject
chemical physics
density functional theory
machine learning
van der Waals
contributor
Kolb, Brian (author)
Thonhauser, Timo (committee chair)
Thonhauser, Timo (committee member)
Salam, Akbar (committee member)
Cook, Gregory B (committee member)
Holzwarth, Natalie (committee member)
Jurchescu, Oana (committee member)
date
2012-09-05T08:35:15Z (accessioned)
2014-09-05T08:30:09Z (available)
2012 (issued)
degree
Physics (discipline)
embargo
2014-09-05 (terms)
identifier
http://hdl.handle.net/10339/37427 (uri)
language
en (iso)
publisher
Wake Forest University
title
An ab Initio Investigation of van der Waals-Rich Systems and a Machine Learning Approach to Finding Analytical Functions Describing Tabulated Data
type
Dissertation

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