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Massachusetts Institute of Technology

Coarse-graining and data mining approaches to the prediction of structures and their dynamics

Abstract

dc:description.abstract

Predicting macroscopic properties of materials starting from an atomistic or electronic level description can be a formidable task due to the many orders of magnitude in length and time scales that need to be spanned. A characteristic of successful approaches to this problem is the systematic coarse-graining of less relevant degrees of freedom in order to obtain Hamiltonians that span larger length and time scale. Attempts to do this in the static regime (i.e. zero temperature) have already been developed, as well as thermodynamical models where all the internal degrees of freedom are removed. In this thesis, we present an approach that leads to a dynamics for thermodynamic-coarse-grained models. This allows us to obtain temperature-dependent and transport properties. The renormalization group theory is used to create new local potential models between nodes, within the approximation of local thermodynamical equilibrium. Assuming that these potentials give an averaged description of node dynamics, we calculate thermal and mechanical properties. If this method can be sufficiently generalized it may form the basis of a Multiscale Molecular Dynamics method with time and spatial coarse-graining. In the second part of the thesis, we analyze the problem of crystal structure prediction, by using quantum calculations.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Materials Science and Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2003

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Curtarolo, Stefano, 1969-
Advisor dc:contributor.advisor
  • Gerbrand Ceder.

Subjects

dc:subject × 1

Rights

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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/17034
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/17034

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Curtarolo, Stefano, 1969-. Coarse-graining and data mining approaches to the prediction of structures and their dynamics. Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17034