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Computational mechanics of macromole...

Schuyler, Adam David.

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Computational mechanics of macromolecules and nanotubes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational mechanics of macromolecules and nanotubes./
作者:	Schuyler, Adam David.
面頁冊數:	184 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6890.
Contained By:	Dissertation Abstracts International66-12B.
標題:	Engineering, Mechanical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3197224
ISBN:	0542430541

Computational mechanics of macromolecules and nanotubes.
Schuyler, Adam David.

Computational mechanics of macromolecules and nanotubes. - 184 p.

Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6890.

Thesis (Ph.D.)--The Johns Hopkins University, 2006.

This dissertation presents new methods for modeling the dynamics of macromolecules and algorithms for efficiently generating random walk distributions on deformed carbon nanotubes. The exponential dimensionality of each system's configuration space often forces analytical tools to become computationally prohibitive. In this dissertation new modeling tools are presented for both problems and are based on re-defining the respective problems in new representation spaces. First, the cluster normal mode analysis (cNMA) tool is derived, and through application, it is shown that the biologically significant motions of protein structures are well captured by comparison to other theoretical and experimental methods. Second, the cNMA tool is applied to extremely large structures ( n &sim; 106 atoms), thus highlighting its O (n) scaling, as compared to more typical methods that are either O (n3) or less detailed. Third, the iterative cluster normal mode analysis (icNMA) tool is derived and used to (i) probe the low-frequency motion space around equilibrium conformations and (ii) produce biologically relevant conformational transition pathways. This tool provides great insight into the structure-function relationship. Fourth, mechanical and electronic properties of deformed and/or (non)homogeneous carbon nanotubes are determined through the enumeration of random walks on lattice structures. These calculations are typically exponential in terms of the walk length m, but the decomposition of the hexagonal lattice into a multi-dimensional integer lattice allows for the usage of the extremely efficient enumeration technique called singe step iterative convolution (SSIC), which reduces the per atom computational complexity to O (m3).

ISBN: 0542430541Subjects--Topical Terms:

783786
Engineering, Mechanical.

Computational mechanics of macromolecules and nanotubes.
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502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2006.
520 $a This dissertation presents new methods for modeling the dynamics of macromolecules and algorithms for efficiently generating random walk distributions on deformed carbon nanotubes. The exponential dimensionality of each system's configuration space often forces analytical tools to become computationally prohibitive. In this dissertation new modeling tools are presented for both problems and are based on re-defining the respective problems in new representation spaces. First, the cluster normal mode analysis (cNMA) tool is derived, and through application, it is shown that the biologically significant motions of protein structures are well captured by comparison to other theoretical and experimental methods. Second, the cNMA tool is applied to extremely large structures ( n &sim; 106 atoms), thus highlighting its O (n) scaling, as compared to more typical methods that are either O (n3) or less detailed. Third, the iterative cluster normal mode analysis (icNMA) tool is derived and used to (i) probe the low-frequency motion space around equilibrium conformations and (ii) produce biologically relevant conformational transition pathways. This tool provides great insight into the structure-function relationship. Fourth, mechanical and electronic properties of deformed and/or (non)homogeneous carbon nanotubes are determined through the enumeration of random walks on lattice structures. These calculations are typically exponential in terms of the walk length m, but the decomposition of the hexagonal lattice into a multi-dimensional integer lattice allows for the usage of the extremely efficient enumeration technique called singe step iterative convolution (SSIC), which reduces the per atom computational complexity to O (m3).
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