Massachusetts Institute of Technology
Atomistic and ab initio prediction and optimization of thermoelectric and photovoltaic properties
Abstract
dc:description.abstractThe accurate prediction of physical properties in the vast spaces of nanoscale structures and chemical compounds is made increasingly possible through the use of atomistic and ab initio computation. In this thesis we investigate lattice thermal conductivities KL and electronic band gaps E,, which are relevant to thermoelectric and photovoltaic applications, respectively, and develop or modify computational tools for predicting and optimizing these properties. For lattice thermal conductivity, we study SiGe nanostructures, which are technologically important for thermoelectric applications. From computing aL for various SiGe nanostructures, we establish that the Kubo-Green approach using classical molecular dynamics (MD) gives additional quantitative predictions not available from phenomenological models, such as the existences of a minimum value of KL as the nanostructure size is varied and of configurational dependence of KL. We carry out the minimizatin of KL in the space of atomic configurations in SiGe alloy nanowires and demonstrate the feasibility of using the cluster expansion technique to parameterize KL. We find that the use of coarse graining and a meta cluster expansion approach is effective, in conjunction with a genetic algorithm, to find configurations which drastically lower KL. The low values of KL obtained, close to the bulk amorphous limit, are due to the absence of long-range order, and such absence allows a local cluster expansion approach to optimize KL. We examine ab initio bandgap prediction for semiconductor compounds, and address the large errors of Kohn-Sham band gaps in density functional theory (DFT).
Degree
thesis:*- Department dc:contributor.department
- Massachusetts Institute of Technology. Dept. of Physics.
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2009
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Chan, Maria Kai Yee
- Advisor dc:contributor.advisor
-
- Gerbrand Ceder and John Joannopoulos.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/53198
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/53198