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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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title
An ab Initio Investigation of van der Waals-Rich Systems and a Machine Learning Approach to Finding Analytical Functions Describing Tabulated Data
author
Kolb, Brian
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
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
type
Dissertation

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