1  10
Number of results to display per page
Online 1. GPU accelerated quantum chemistry [electronic resource] [2015]
 Luehr, Nathan.
 2015.
 Description
 Book — 1 online resource.
 Summary

This dissertation develops techniques to accelerate quantum chemistry calculations using commodity graphical processing units (GPUs). As both the principle bottleneck in finite basis calculations and a highly parallel task, the evaluation of Gaussian integrals is a prime target for GPU acceleration. Methods to tailor quantum chemistry algorithms from the bottom up to take maximum advantage of massively parallel processors are described. Special attention is taken to make maximum use of performance features typical of modern GPUs, such as high single precision performance. After developing an efficient integral direct selfconsistent field (SCF) procedure for GPUs that is an order of magnitude faster than typical CPU codes, the same machinery is extended to the configuration interaction singles (CIS) and time dependent density functional theory (TDDFT) methods. Finally, this machinery is applied to molecular dynamics (MD) calculations. To extend the time scale accessible to MD calculations of large systems, an ab initio multiple time steps (MTS) approach is developed. For small systems, up to a few dozen atoms, an interactive interface enabling a virtual molecular modeling kit complete with realistic ab initio forces is developed.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2015 L  Inlibrary use 
 Nakamura, Muneaki.
 2014.
 Description
 Book — 1 online resource.
 Summary

Cytoskeletal motors are involved in a vast array of cellular processes, including motility, cargo transport, and force generation. Molecular engineering stands as a useful tool to interrogate the determinants of motor protein activity. Moreover, in order to harness these molecular machines for nanotechnological functions, as well as to perturb the cellular functions dependent on these motors, I have endeavored to develop novel methods of control over the motion generated by both actin and microtubulebased motors. I first undertook a dissection of the determinants of directionality in an actinbased motor, myosin VI. Based on these results, we were able to identify a strategy for dynamic control over myosin VI directionality based on reversible transitions between rigid and flexible structural states of a portion of the myosin lever arm. We created a calciumsensitive bidirectional myosin as an initial demonstration of this strategy by fusing calciumsensitive calmodulinbinding domains to the lever arm. We subsequently extended lever arm engineering to encompass light control through the incorporation of a lightsensitive protein domain (LOV2) into the lever arm of myosin. Junctional variants and computational design yielded constructs that speed up, slow down, and switch directions in response to blue light. To test the generality of our opticallycontrolled design, we fused our engineered lever arm to myosin XI to create controllable myosins with high velocities, and to kinesin14 (Ncd) to create controllable microtubulebased motion. These engineered motors could serve as the basis for components of nanoscale devices or for optogenetic control over cytoskeletal motor function in living cells.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2014 N  Inlibrary use 
Online 3. Bayesian analysis for reversible time series with applications to molecular dynamics simulation [electronic resource] [2012]
 Description
 Book — 1 online resource.
 Summary

A host of sequential models in probability and statistics are characterized by time reversibility, from Markov chain Monte Carlo samplers to queueing networks. In physics, this property arises naturally from Hamiltonian mechanics. Molecular dynamics simulations are computer experiments which approximate classical mechanics in a system of interacting particles; in consequence, they are frequently reversible. Recent technical progress has made it possible to investigate the dynamics of biological macromolecules in silico using molecular dynamics simulations. An active area of research within this field is concerned with modeling the output of a simulation stochastically. This dissertation deals with the problem of incorporating knowledge of reversibility into the estimation and testing of stochastic models. We define a range of Bayesian inference algorithms, which are motivated by specific problems in the analysis of molecular dynamics simulations.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2012 L  Inlibrary use 
Online 4. Finite representation of an infinite molecular system [electronic resource] : algorithms and applications [2012]
 Wagoner, Jason Alan.
 2012.
 Description
 Book — 1 online resource.
 Summary

The utility of molecular simulation techniques relies on the ability to accurately and efficiently explore the appropriate distribution of configurations. For this reason, many simulation techniques use approximations that reduce the dimensionality of a system. The result is a molecular representation with fewer degrees of freedom than would otherwise be included. This dissertation explores a number of methods aimed at reducing the dimensionality for various molecular systems. The main focus of this work is the development of a new hybrid explicit/implicit solvent model capable of accurately reproducing solvation effects with significantly fewer explicitly represented molecules.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2012 W  Inlibrary use 
Online 5. A diagrammatic kinetic theory for describing long timescale correlations in dense, simple liquids in the overdamped limit [electronic resource] [2011]
 Description
 Book — 1 online resource.
 Summary

Starting from an exact diagrammatic theory for density correlations in dense, atomic fluids, we derive a set of graphical approximations to this theory that are consistent with a set of physical assumptions that define an overdamped limit of the dynamics of the system. The results of a simple one loop approximation to this theory are then compared with data from molecular dynamics simulations for a number of correlation functions of a simple LennardJones fluid at a single, high density and a range of temperatures. For correlation functions that have most of their decay over times for which the overdamped theory is valid, the one loop approximation gives accurate results, except for coherent correlation functions at small wavevector, for which the overdamped theory is not expected to be accurate. Although the temperature range we studied included only temperatures at or above the liquid's triple point, it is our hope that the overdamped theory can ultimately be used to characterize the dynamics of supercooled liquids. This will certainly require going beyond the one loop approximation.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2011 P  Inlibrary use 
Online 6. A lattice model of the translational dynamics of nonrotating rigid rods [electronic resource] [2011]
 Tse, Ying Lung.
 2011.
 Description
 Book — 1 online resource.
 Summary

We present a lattice model of oriented, nonrotating, rigid rods in three dimensions with random walk dynamics and an algorithm to simulate the model. We use the ideas of the DoiEdwards (DE) theory, which was originally developed for a system of rods that both translate and rotate in continuous space, to predict the dependence of the translational diffusion constant of the rods in the perpendicular direction, on the (dimensionless) concentration in the semidilute regime. We find that the transnational perpendicular diffusion constant is proportional to the inverse square of the concentration. The theory is based on a `tube model' for the constraints imposed on the motion of a rod by the surrounding rods. Simulations of the model confirm that the scaling predicted by DE ideas and that the nature of the agreement is similar to that for the rotational diffusion constant in the original DE theory. We formulate a quantitative theory for the prefactor in the scaling relationship using only DE ideas, but it predicts a proportionality constant that is much too small. To explain this discrepancy, we modify the DE approach to obtain a more accurate estimate of the average tube radius, and we take into account two effects, called `leakage' and `drift', that are caused by perpendicular motions of rods that are ignored by the original DE theory. The theory of leakage takes into account the fact that the ends of a rod are less effective than the middle of the rod for blocking the motion of nearby rods. The theory of drift takes into account that the tube that any one rod is in can move in the perpendicular direction without changing its structure as a result of the perpendicular motion of the rods that form the tube. With these changes, the theory predicts a prefactor that is in much better agreement with the simulations. The simulations find that, as the concentration is increased, the approach to the limit of DE scaling is slow, and the 2 power in the DE scaling law is never quite achieved even at the highest concentration simulated. We propose a new scaling relationship that explains the deviations from the DE scaling relationship. Finally, we study the self and total densitydensity space time correlation functions for this model and propose a simple theory for the short time behavior of these functions based on a onedimensional twocomponent lattice gas model.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2011 T  Inlibrary use 
Online 7. Statistical mechanical basis and algorithms for constructing coarse grained models of molecular liquids and biological structures [electronic resource] [2011]
 Das, Avisek.
 2011.
 Description
 Book — 1 online resource.
 Summary

Computer simulation of condensed phases have been on the forefront of the scientific research in varieties of disciplines including but not limited to chemistry, physics, biophysics and material science. In spite of impressive success, standard computational techniques such as molecular dynamics and Monte Carlo run into serious limitations in the system sizes and temporal durations accessible in simulations when an atomically detailed description of the system is employed, for any given choice of computational technology. One obvious way of surmounting this problem is to construct a reduced representation of the original system of interest with the hope that reduction in number of degrees of freedom will cut down the computational cost at the same time retaining some essential features. This approach, known as coarsegrained modelling, has attracted a great deal of attention in recent years and a varieties of methods have been reported in the literature. The multiscale coarsegraining (MSCG) method is a method for determining the effective potential energy function for a coarsegrained (CG) model of a system using data obtained from molecular dynamics simulation of the corresponding atomically detailed model. The method has been given a rigorous statistical mechanical basis and the coarsegrained potential obtained using the MSCG method is an approximate variational solution for the exact manybody potential of mean force for the coarsegrained sites. In this thesis we extend the formal theory behind the method to situations that were not considered in the original version, thereby expanding the applicability of the method. We also develop new algorithms for practical implementation of the MSCG method. The algorithmic developments consist of introduction of new basis functions for representing the CG potential energy functions and construction of new numerical techniques for the optimization problem associated with the MSCG method. We apply the MSCG method, with a new set of basis functions, to study the many body potential of mean force among solutes in a simple model of a solution of LennardJones particles. For this model, pairwise additivity of the many body potential of mean force is a very good approximation when the solute concentration is low, and it becomes less accurate for high concentrations, indicating the importance of many body contributions to the coarsegrained potential. We propose and test a version of the MSCG method suitable for the isothermalisobaric ensemble. The method shows how to construct an effective potential energy function for a coarsegrained system that generates the correct volume fluctuations as well as correct distribution functions in the configuration space of the CG sites. We present a new numerical algorithm with automatic basis set selection and noise suppression capabilities for the solution of the MSCG variational problem. We also develop new basis functions that are similar to multiresolution Haar functions and that have the differentiability properties that are appropriate for representing CG potentials. The new method, allows us to construct a large basis set, and the method automatically chooses a subset of the basis that is most important for representing the MSCG potential. It provides regularization to mitigate potential numerical problems in the associated linear least squares calculation, and it provides a way to avoid fitting statistical error. We use this technology to construct a systematic method for including three body terms as well as two body terms in the nonbonded part of the CG potential energy. Inclusion of three body terms can lead to significant improvement in the accuracy of CG potentials and hence of CG simulations as shown by the test calculations on two very different model systems. We construct basis functions for representing the CG potential energy functions for molecular systems. We also discuss the problem arising from insufficient sampling of certain parts of the atomistic configuration space and develop methods for surmounting this problem that require very little human intervention. We test our algorithms on a simple but nontrivial test problem that involves constructing coarse grained models of liquid hexane.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2011 D  Inlibrary use 
Online 8. A Bayesian method for construction of Markov models to describe dynamics on various time scales [electronic resource] [2011]
 Rains, Emily Kathleen.
 2010, c2011.
 Description
 Book — 1 online resource.
 Summary

The dynamics of many biological processes of interest, such as the folding of a protein, are slow and complicated enough that a single molecular dynamics simulation trajectory of the entire process is difficult to obtain in any reasonable amount of time. Moreover, one such simulation may not be sufficient to develop an understanding of the mechanism of the process, and multiple simulations may be necessary. One approach to circumvent this computational barrier is the use of Markov state models. These models are useful because they can be constructed using data from a large number of shorter simulations instead of a single long simulation. This thesis presents a new Bayesian method for the construction of Markov models from simulation data. A Markov model is specified by (t, P, T), where t is the mesoscopic time step, P is a partition of configuration space into mesostates, and T is an N x N transition rate matrix for transitions between the mesostates in one mesoscopic time step, where N is the number of mesostates in P. The method presented here is different from previous Bayesian methods in several ways. 1. The method uses Bayesian analysis to determine the partition as well as the transition probabilities. 2. The method allows the construction of a Markov model for any chosen mesoscopic time scale t. 3. It constructs Markov models for which the diagonal elements of T are all equal to or greater than 0.5. Such a model will be called a 'consistent mesoscopic Markov model' (or CMMM). Such models have important advantages for providing an understanding of the dynamics on a mesoscopic time scale. The Bayesian method uses simulation data to find a posterior probability distribution for (P, T) for any chosen t. This distribution can be regarded as the Bayesian probability that the kinetics observed in the atomistic simulation data on the mesoscopic time scale t was generated by the CMMM specified by (P, T). An optimization algorithm is used to find the most probable CMMM for the chosen mesoscopic time step. We applied this method of Markov model construction to several toy systems (random walks in one and two dimensions) as well as the dynamics of alanine dipeptide in water and of trpzip2 in water. The resulting Markov state models were indeed successful in capturing the dynamics of our test systems on a variety of mesoscopic time scales.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2010 R  Inlibrary use 
 Ensign, Daniel L.
 2010.
 Description
 Book — 1 online resource.
 Summary

Bayesian statistics is a powerful method for inferencepossibly the uniquely correct method for inference. As described herein, when applied to a few degrees of freedom from single molecule trajectories, Bayesian statistics yield useful insights into rates, states, and motions.
 Also online at

Special Collections
Special Collections  Status 

University Archives  Request onsite access 
3781 2010 E  Inlibrary use 
Online 10. Bayesian analysis for reversible time series with applications to molecular dynamics simulation [2012]
 Bacallado de Lara, Sergio Andres.
 Aug. 2012.
 Description
 Book — online resource (xiv, 167 pages) : illustrations (some color)
 Summary

A host of sequential models in probability and statistics are characterized by time reversibility, from Markov chain Monte Carlo samplers to queueing networks. In physics, this property arises naturally from Hamiltonian mechanics. Molecular dynamics simulations are computer experiments which approximate classical mechanics in a system of interacting particles; in consequence, they are frequently reversible. Recent technical progress has made it possible to investigate the dynamics of biological macromolecules in silico using molecular dynamics simulations. An active area of research within this field is concerned with modeling the output of a simulation stochastically. This dissertation deals with the problem of incorporating knowledge of reversibility into the estimation and testing of stochastic models. We define a range of Bayesian inference algorithms, which are motivated by specific problems in the analysis of molecular dynamics simulations.
 Also online at
Medical Library (Lane)
Medical Library (Lane)  Status 

Check Lane Library catalog for status  
(no call number)  Unknown 
Articles+
Journal articles, ebooks, & other eresources
 Articles+ results include