Molecular dynamics is a technique in which the trajectories of a group of particles are calculated as a function of time by integrating the equations of motion. In this thesis we study the use of molecular dynamics for atoms in a crystal. A model is introduced which describes interactions of a physical system with an external heat reservoir in molecular dynamics simulations. This is accomplished by the addition of a "virtual variable" to the Hamiltonian which is used to scale time. Aspects of this model are discussed and examples are presented for a simple system. Similarly, a constant pressure model is introduced in which additional virtual volume variables are added to the Hamiltonian. The volume and shape of the molecular dynamics cell are now free to vary. Simple examples are discussed. Aspects of the computer programs and the algorithms are explained. Particular attention is focused on the methods used to integrate the equations of motion and to calculate the coulomb interactions. Examples of simulations using a zirconium oxide crystal are presented. We study the effects of heat bath and pressure bath simulations, both separately and in combination. Various features of the behavior are investigated with the primary focus on phase changes, numerical errors, and parameters describing the heat and pressure baths. / Graduation date: 1996

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Water is the most common solvent for most biological reactions, therefore it is vital that we fully understand water and all its properties. The complex hydrogen bonding network that water forms can influence protein-protein and protein-substrate interactions and can slow protein conformational shifts. Here, I examine an important property of water known as energetic roughness. The network of interactions between individual water molecules affect the energy landscape of proteins by altering the underlying energetic roughness. I have attributed this roughness to the making and breaking of hydrogen bonds as the network of hydrogen bonds constantly adopts new conformations. Through a novel computational approach I have analyzed five prominent water models and have determined their inherent roughness to be between 0.43 and 0.62 kcal/mol.

Nanocomposites refer to the materials in which the defining characteristic size of inclusions is in the order of 10-100nm. There are several types of nanoparticle inclusions with different structures: metal clusters, fullerenes particles and molybdenum selenide, Our research focus is on polymer nanocomposites with inorganic clay particles as inclusions, in particular we used sodium montmorillonite polymer nanocomposite. In our study, modeling and simulations of sodium montmorillonite (Na+-MMT) is currently being investigated as an inorganic nanocomposite material. Na+-MMT clay consists of platelets, one nanometer thick with large lateral dimensions, which can be used to achieve efficient reinforcement of polymer matrices. This nanocomposite has different applications such as a binder of animal feed, a plasticizing agent in cement, brick and ceramic, and a thickener and stabilizer of latex and rubber adhesives. In this study, sodium montmorillonite called Na+-MMT structure is built with the bulk system and the layered system which includes from 1 to 12 layers by using Crystal Builder of Cerius2. An isothermal and isobaric ensemble is used for calculation of thermodynamic properties such as specific heat capacities and isothermal expansion coefficients of Na+-MMT. A canonical ensemble which holds a fixed temperature, volume and number of molecules is used for defining exfoliation kinetics of layered structures and surface formation energies for Na+-MMT layered structures are calculated by using a canonical ensemble. Mechanical properties are used to help characterize and identify the Na+-MMT structure. Several elastic properties such as compliance and stiffness matrices, Young's, shear, and bulk modulus, volume compressibility, Poisson's ratios, Lamé constants, and velocities of sound are calculated in specified directions. Another calculation method is the Vienna Ab-initio Simulation Package (VASP). VASP is a complex package for performing ab-initio quantum-mechanical calculations and molecular dynamic (MD) simulations using pseudopotentials and a plane wave basis set. Cut off energy is optimized for the unit cell of Na+-MMT by using different cut off energy values. Experimental and theoretical cell parameters are compared by using cell shape and volume optimization and root mean square (RMS) coordinate difference is calculated for variation of cell parameters. Cell shape and volume optimization are done for calculating optimum expansion or compression constant.

Thesis (M.Sc.)--Naval Postgraduate School, September 2003. / Includes bibliographical references.

Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Young W. Kwon. Includes bibliographical references (p. 49-50). Also available online.

Thesis (Ph. D.)--Florida State University, 2003. / Advisor: Dr. Michael S. Chapman, Florida State University, College of Arts and Sciences, Institute of Molecular Biophysics. Title and description from dissertation home page (viewed Mar. 11, 2004). Includes bibliographical references.

In recent years there has been a significant development of the dynamical map formalism for initially correlated states of a system and its environment. Based on some of these results, we study quantum process tomography for initially correlated states of the system and the environment. This is beyond the usual assumption that the state of the system and the environment are initially uncorrelated. Since quantum process tomography is an experimental procedure, we wind up having to study the role of preparation of input states for open quantum experiments. We work out a theory for the general preparation procedure, and study two preparation procedures in detail. In specific, we study the stochastic preparation procedure and the projective preparation procedure and apply them to quantum process tomography. The two preparation procedures describe the ways to uncorrelate the state of the system and the environment. However the specifics of how this is implemented plays a role on the outcomes of the experiment. When the stochastic preparation procedure is applied properly, quantum process tomography yields a linear process maps. We point out what it means to apply the stochastic preparation procedure properly by constructing several simple examples where inconsistencies in preparations leads to errors. When the projective preparation procedure is applied, quantum process tomography leads to a non-linear process map. We show that these processes can only be consistently described by a general dynamical map, which we call M-map. The M-map contains all of the dynamical information for the state of the system without the affects of a preparation procedure. By carefully extracting some of this dynamical information, we construct a quantitative measure for the memory effect due to the initial correlations with the environment. / text

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