13,501 research outputs found
ATK-ForceField: A New Generation Molecular Dynamics Software Package
ATK-ForceField is a software package for atomistic simulations using
classical interatomic potentials. It is implemented as a part of the Atomistix
ToolKit (ATK), which is a Python programming environment that makes it easy to
create and analyze both standard and highly customized simulations. This paper
will focus on the atomic interaction potentials, molecular dynamics, and
geometry optimization features of the software, however, many more advanced
modeling features are available. The implementation details of these algorithms
and their computational performance will be shown. We present three
illustrative examples of the types of calculations that are possible with
ATK-ForceField: modeling thermal transport properties in a silicon germanium
crystal, vapor deposition of selenium molecules on a selenium surface, and a
simulation of creep in a copper polycrystal.Comment: 28 pages, 9 figure
WavePacket: A Matlab package for numerical quantum dynamics. I: Closed quantum systems and discrete variable representations
WavePacket is an open-source program package for the numerical simulation of
quantum-mechanical dynamics. It can be used to solve time-independent or
time-dependent linear Schr\"odinger and Liouville-von Neumann-equations in one
or more dimensions. Also coupled equations can be treated, which allows to
simulate molecular quantum dynamics beyond the Born-Oppenheimer approximation.
Optionally accounting for the interaction with external electric fields within
the semiclassical dipole approximation, WavePacket can be used to simulate
experiments involving tailored light pulses in photo-induced physics or
chemistry.The graphical capabilities allow visualization of quantum dynamics
'on the fly', including Wigner phase space representations. Being easy to use
and highly versatile, WavePacket is well suited for the teaching of quantum
mechanics as well as for research projects in atomic, molecular and optical
physics or in physical or theoretical chemistry.The present Part I deals with
the description of closed quantum systems in terms of Schr\"odinger equations.
The emphasis is on discrete variable representations for spatial discretization
as well as various techniques for temporal discretization.The upcoming Part II
will focus on open quantum systems and dimension reduction; it also describes
the codes for optimal control of quantum dynamics.The present work introduces
the MATLAB version of WavePacket 5.2.1 which is hosted at the Sourceforge
platform, where extensive Wiki-documentation as well as worked-out
demonstration examples can be found
How dissipation constrains fluctuations in nonequilibrium liquids: Diffusion, structure and biased interactions
The dynamics and structure of nonequilibrium liquids, driven by
non-conservative forces which can be either external or internal, generically
hold the signature of the net dissipation of energy in the thermostat. Yet,
disentangling precisely how dissipation changes collective effects remains
challenging in many-body systems due to the complex interplay between driving
and particle interactions. First, we combine explicit coarse-graining and
stochastic calculus to obtain simple relations between diffusion, density
correlations and dissipation in nonequilibrium liquids. Based on these results,
we consider large-deviation biased ensembles where trajectories mimic the
effect of an external drive. The choice of the biasing function is informed by
the connection between dissipation and structure derived in the first part.
Using analytical and computational techniques, we show that biasing
trajectories effectively renormalizes interactions in a controlled manner, thus
providing intuition on how driving forces can lead to spatial organization and
collective dynamics. Altogether, our results show how tuning dissipation
provides a route to alter the structure and dynamics of liquids and soft
materials.Comment: 21 pages, 7 figure
JDFTx: software for joint density-functional theory
Density-functional theory (DFT) has revolutionized computational prediction
of atomic-scale properties from first principles in physics, chemistry and
materials science. Continuing development of new methods is necessary for
accurate predictions of new classes of materials and properties, and for
connecting to nano- and mesoscale properties using coarse-grained theories.
JDFTx is a fully-featured open-source electronic DFT software designed
specifically to facilitate rapid development of new theories, models and
algorithms. Using an algebraic formulation as an abstraction layer, compact
C++11 code automatically performs well on diverse hardware including GPUs. This
code hosts the development of joint density-functional theory (JDFT) that
combines electronic DFT with classical DFT and continuum models of liquids for
first-principles calculations of solvated and electrochemical systems. In
addition, the modular nature of the code makes it easy to extend and interface
with, facilitating the development of multi-scale toolkits that connect to ab
initio calculations, e.g. photo-excited carrier dynamics combining electron and
phonon calculations with electromagnetic simulations.Comment: 9 pages, 3 figures, 2 code listing
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
Microtubule dynamics depart from wormlike chain model
Thermal shape fluctuations of grafted microtubules were studied using high
resolution particle tracking of attached fluorescent beads. First mode
relaxation times were extracted from the mean square displacement in the
transverse coordinate. For microtubules shorter than 10 um, the relaxation
times were found to follow an L^2 dependence instead of L^4 as expected from
the standard wormlike chain model. This length dependence is shown to result
from a complex length dependence of the bending stiffness which can be
understood as a result of the molecular architecture of microtubules. For
microtubules shorter than 5 um, high drag coefficients indicate contributions
from internal friction to the fluctuation dynamics.Comment: 4 pages, 4 figures. Updated content, added reference, corrected typo
A Domain-Specific Language and Editor for Parallel Particle Methods
Domain-specific languages (DSLs) are of increasing importance in scientific
high-performance computing to reduce development costs, raise the level of
abstraction and, thus, ease scientific programming. However, designing and
implementing DSLs is not an easy task, as it requires knowledge of the
application domain and experience in language engineering and compilers.
Consequently, many DSLs follow a weak approach using macros or text generators,
which lack many of the features that make a DSL a comfortable for programmers.
Some of these features---e.g., syntax highlighting, type inference, error
reporting, and code completion---are easily provided by language workbenches,
which combine language engineering techniques and tools in a common ecosystem.
In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL
and development environment for numerical simulations based on particle methods
and hybrid particle-mesh methods. PPME uses the meta programming system (MPS),
a projectional language workbench. PPME is the successor of the Parallel
Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional
implementation strategies. We analyze and compare both languages and
demonstrate how the programmer's experience can be improved using static
analyses and projectional editing. Furthermore, we present an explicit domain
model for particle abstractions and the first formal type system for particle
methods.Comment: Submitted to ACM Transactions on Mathematical Software on Dec. 25,
201
TinkerCell: Modular CAD Tool for Synthetic Biology
Synthetic biology brings together concepts and techniques from engineering
and biology. In this field, computer-aided design (CAD) is necessary in order
to bridge the gap between computational modeling and biological data. An
application named TinkerCell has been created in order to serve as a CAD tool
for synthetic biology. TinkerCell is a visual modeling tool that supports a
hierarchy of biological parts. Each part in this hierarchy consists of a set of
attributes that define the part, such as sequence or rate constants. Models
that are constructed using these parts can be analyzed using various C and
Python programs that are hosted by TinkerCell via an extensive C and Python
API. TinkerCell supports the notion of a module, which are networks with
interfaces. Such modules can be connected to each other, forming larger modular
networks. Because TinkerCell associates parameters and equations in a model
with their respective part, parts can be loaded from databases along with their
parameters and rate equations. The modular network design can be used to
exchange modules as well as test the concept of modularity in biological
systems. The flexible modeling framework along with the C and Python API allows
TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is
a free and open-source project under the Berkeley Software Distribution
license. Downloads, documentation, and tutorials are available at
www.tinkercell.com.Comment: 23 pages, 20 figure
Equilibrium susceptibilities of superparamagnets: longitudinal & transverse, quantum & classical
The equilibrium susceptibility of uniaxial paramagnets is studied in a
unified framework which permits to connect traditional results of the theory of
quantum paramagnets, \Sm=1/2, 1, 3/2, ..., with molecular magnetic clusters,
\Sm\sim5, 10, 20, all the way up, \Sm=30, 50, 100,... to the theory of
classical superparamagnets. This is done using standard tools of quantum
statistical mechanics and linear response theory (the Kubo correlator
formalism). Several features of the temperature dependence of the
susceptibility curves (crossovers, peaks, deviations from Curie law) are
studied and their scalings with \Sm identified and characterized. Both the
longitudinal and transverse susceptibilities are discussed, as well as the
response of the ensemble with anisotropy axes oriented at random. For the
latter case a simple approximate formula is derived too, and its range of
validity assessed, so it could be used in modelization of experiments.Comment: 32 pages, 5 figures. Submitted to J.Phys.Condens.Matte
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