nMoldyn - Interfacing spectroscopic experiments, molecular dynamics simulations and models for time correlation functions
01 Jan 2011-Vol. 12, pp 201-232
TL;DR: A synoptic view of the range of applications of the latest version of nMoldyn is presented, which includes new modules for a simulation-based interpretation of data from nuclear magnetic resonance spectroscopy, far infraredSpectroscopy and for protein secondary structure analysis.
Abstract: This article gives an introduction into the program nMoldyn, which has been originally conceived to support the interpretation of neutron scattering experiments on complex molecular systems by the calculation of appropriate time correlation functions from classical and quantum molecular dynamics simulations of corresponding model systems. Later the functionality has been extended to include more advanced time series analyses of molecular dynamics trajectories, in particular the calculation of memory functions, which play an essential role in the theory of time correlation functions. Here we present a synoptic view of the range of applications of the latest version of nMoldyn, which includes new modules for a simulation-based interpretation of data from nuclear magnetic resonance spectroscopy, far infrared spectroscopy and for protein secondary structure analysis.
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TL;DR: The parts of this book of most interest and value to the EMC engineer will be the chapters on Thermal Noise, Antennas, Propagation and Transmission Lines, and Reflection and Refraction.
Abstract: dix A. Even if you don’t choose to memorize them this system aids in reference and retreival of important formulas. The book was compiled from notes developed during eight years of teaching a graduate course on the subject and was used as a text. Thus it has been student tested. Appendix F contains a number of problems, grouped to be used on a chapter by chapter basis The problems are designed to illustrate practical applications of the text material. The parts of this book of most interest and value to the EMC engineer will be the chapters on Thermal Noise, Antennas, Propagation and Transmission Lines, and Reflection and Refraction. This is not to downpade the chapters on Statistics and Its Applications, Signal Processing and Detection, and Some System Characteristics which also contain much potentially useful materials. Additional plus values for the book include a list of 40 references, a table of symbols used throughout the book, and a subject index. Some readers may find the condensed type and close line spacing hard to read. It was apparently set up by typewriter using an elite type face with single line spacing. When reduced down to a 6 by 9 5 inch size page it is too crowded for easy reading. In spite of this shortcoming your reviewer recommends this book as a worthwhile reference in this field of interest.
413 citations
TL;DR: Nine years after the original publication of TRAVIS, some of the recent new functions and features are highlighted, which contribute to make trajectory analysis more efficient.
Abstract: TRAVIS (“Trajectory Analyzer and Visualizer”) is a program package for post-processing and analyzing trajectories from molecular dynamics and Monte Carlo simulations, mostly focused on molecular condensed phase systems. It is an open source free software licensed under the GNU GPL, is platform independent, and does not require any external libraries. Nine years after the original publication of TRAVIS, we highlight some of the recent new functions and features in this article. At the same time, we shortly present some of the underlying algorithms in TRAVIS, which contribute to make trajectory analysis more efficient. Some modern visualization techniques such as Sankey diagrams are also demonstrated. Many analysis functions are implemented, covering structural analyses, dynamical analyses, and functions for predicting vibrational spectra from molecular dynamics simulations. While some of the analyses are known since several decades, others are very recent. For example, TRAVIS has been used to compute the first ab initio predictions in the literature of bulk phase vibrational circular dichroism spectra, bulk phase Raman optical activity spectra, and bulk phase resonance Raman spectra within the last few years.
263 citations
TL;DR: The freud Python package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools, enabling analysis of a broader class of data ranging from biomolecular simulations to colloidal experiments.
Abstract: The freud Python package is a library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods such as the potential of mean force and torque and local crystal environment analysis with equal ease. Rather than providing its own trajectory data structure, freud operates either directly on NumPy arrays or on trajectory data structures provided by other Python packages. This design allows freud to transparently interface with many trajectory file formats by leveraging the file parsing abilities of other trajectory management tools. By remaining agnostic to its data source, freud is suitable for analyzing any particle simulation, regardless of the original data representation or simulation method. When used for on-the-fly analysis in conjunction with scriptable simulation software such as HOOMD-blue, freud enables smart simulations that adapt to the current state of the system, allowing users to study phenomena such as nucleation and growth. Program summary Program Title: freud Program Files doi: http://dx.doi.org/10.17632/v7wmv9xcct.1 Licensing provisions: BSD 3-Clause Programming language: Python, C++ Nature of problem: Simulations of coarse-grained, nano-scale, and colloidal particle systems typically require analyses specialized to a particular system. Certain more standardized techniques – including correlation functions, order parameters, and clustering – are computationally intensive tasks that must be carefully implemented to scale to the larger systems common in modern simulations. Solution method: freud performs a wide variety of particle system analyses, offering a Python API that interfaces with many other tools in computational molecular sciences via NumPy array inputs and outputs. The algorithms in freud leverage parallelized C++ to scale to large systems and enable real-time analysis. The library’s broad set of features encode few assumptions compared to other analysis packages, enabling analysis of a broader class of data ranging from biomolecular simulations to colloidal experiments. Additional comments including restrictions and unusual features: 1. freud provides very fast parallel implementations of standard analysis methods like RDFs and correlation functions. 2. freud includes the reference implementation for the potential of mean force and torque (PMFT). 3. freud provides various novel methods for characterizing particle environments, including the calculation of descriptors useful for machine learning. The source code is hosted on GitHub ( https://github.com/glotzerlab/freud ), and documentation is available online ( https://freud.readthedocs.io/ ). The package may be installed via pip install freud-analysis or conda install -c conda-forge freud .
114 citations
TL;DR: The dependence of single-particle diffusion coefficients on the size and shape of the simulation box in molecular dynamics simulations of fluids and lipid membranes is investigated and it is found that the diffusion coefficients of lipids and a carbon nanotube embedded in a lipid membrane diverge with the logarithm of the box width.
Abstract: We investigate the dependence of single-particle diffusion coefficients on the size and shape of the simulation box in molecular dynamics simulations of fluids and lipid membranes. We find that the diffusion coefficients of lipids and a carbon nanotube embedded in a lipid membrane diverge with the logarithm of the box width. For a neat Lennard-Jones fluid in flat rectangular boxes, diffusion becomes anisotropic, diverging logarithmically in all three directions with increasing box width. In elongated boxes, the diffusion coefficients normal to the long axis diverge linearly with the height-to-width ratio. For both lipid membranes and neat fluids, this behavior is predicted quantitatively by hydrodynamic theory. Mean-square displacements in the neat fluid exhibit intermediate regimes of anomalous diffusion, with t ln t and t3/2 components in flat and elongated boxes, respectively. For membranes, the large finite-size effects, and the apparent inability to determine a well-defined lipid diffusion coefficien...
67 citations
References
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TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Abstract: A general method, suitable for fast computing machines, for investigating such properties as equations of state for substances consisting of interacting individual molecules is described. The method consists of a modified Monte Carlo integration over configuration space. Results for the two‐dimensional rigid‐sphere system have been obtained on the Los Alamos MANIAC and are presented here. These results are compared to the free volume equation of state and to a four‐term virial coefficient expansion.
35,161 citations
"nMoldyn - Interfacing spectroscopic..." refers background in this paper
...[1] to study the equation of state of model liquids and the pioneering molecular dynamics (MD) study of liquid argon by Rahman, which extended the scope of computer simulations to time dependent phenomena [2]....
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TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Abstract: The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
34,239 citations
"nMoldyn - Interfacing spectroscopic..." refers methods in this paper
...The lysozyme structure was taken from the Brookhaven protein data bank [28] (code 193L[29]) and hydrogen atoms were added to the structure according to standard criteria concerning the chemical bond structure of amino acids....
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TL;DR: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms.
Abstract: An N⋅log(N) method for evaluating electrostatic energies and forces of large periodic systems is presented. The method is based on interpolation of the reciprocal space Ewald sums and evaluation of the resulting convolutions using fast Fourier transforms. Timings and accuracies are presented for three large crystalline ionic systems.
24,332 citations
"nMoldyn - Interfacing spectroscopic..." refers methods in this paper
...To mimic a macroscopic system, periodic boundary conditions have been applied and electrostatic interactions have been computed using the particle mesh Ewald method (PME) [53], with a cut-off of 12 Å....
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Book•
01 Jan 1986
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Abstract: Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. Normalized Least-Mean-Square Adaptive Filters. 7. Transform-Domain and Sub-Band Adaptive Filters. 8. Method of Least Squares. 9. Recursive Least-Square Adaptive Filters. 10. Kalman Filters as the Unifying Bases for RLS Filters. 11. Square-Root Adaptive Filters. 12. Order-Recursive Adaptive Filters. 13. Finite-Precision Effects. 14. Tracking of Time-Varying Systems. 15. Adaptive Filters Using Infinite-Duration Impulse Response Structures. 16. Blind Deconvolution. 17. Back-Propagation Learning. Epilogue. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. Appendix C. Method of Lagrange Multipliers. Appendix D. Estimation Theory. Appendix E. Eigenanalysis. Appendix F. Rotations and Reflections. Appendix G. Complex Wishart Distribution. Glossary. Abbreviations. Principal Symbols. Bibliography. Index.
16,062 citations
"nMoldyn - Interfacing spectroscopic..." refers methods in this paper
...one obtains finally an approximation for the Fourier spectrum of the ACF of a(t), which is based on the AR model [61, 64],...
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TL;DR: NAMD as discussed by the authors is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems that scales to hundreds of processors on high-end parallel platforms, as well as tens of processors in low-cost commodity clusters, and also runs on individual desktop and laptop computers.
Abstract: NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.
14,558 citations
"nMoldyn - Interfacing spectroscopic..." refers methods in this paper
...The MD trajectory used for the calculation nMoldyn has been computed by the program package NAMD [51]....
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