37,581 research outputs found
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
Variable-free exploration of stochastic models: a gene regulatory network example
Finding coarse-grained, low-dimensional descriptions is an important task in
the analysis of complex, stochastic models of gene regulatory networks. This
task involves (a) identifying observables that best describe the state of these
complex systems and (b) characterizing the dynamics of the observables. In a
previous paper [13], we assumed that good observables were known a priori, and
presented an equation-free approach to approximate coarse-grained quantities
(i.e, effective drift and diffusion coefficients) that characterize the
long-time behavior of the observables. Here we use diffusion maps [9] to
extract appropriate observables ("reduction coordinates") in an automated
fashion; these involve the leading eigenvectors of a weighted Laplacian on a
graph constructed from network simulation data. We present lifting and
restriction procedures for translating between physical variables and these
data-based observables. These procedures allow us to perform equation-free
coarse-grained, computations characterizing the long-term dynamics through the
design and processing of short bursts of stochastic simulation initialized at
appropriate values of the data-based observables.Comment: 26 pages, 9 figure
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Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.
Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally
Constrained Approximation of Effective Generators for Multiscale Stochastic Reaction Networks and Application to Conditioned Path Sampling
Efficient analysis and simulation of multiscale stochastic systems of
chemical kinetics is an ongoing area for research, and is the source of many
theoretical and computational challenges. In this paper, we present a
significant improvement to the constrained approach, which is a method for
computing effective dynamics of slowly changing quantities in these systems,
but which does not rely on the quasi-steady-state assumption (QSSA). The QSSA
can cause errors in the estimation of effective dynamics for systems where the
difference in timescales between the "fast" and "slow" variables is not so
pronounced.
This new application of the constrained approach allows us to compute the
effective generator of the slow variables, without the need for expensive
stochastic simulations. This is achieved by finding the null space of the
generator of the constrained system. For complex systems where this is not
possible, or where the constrained subsystem is itself multiscale, the
constrained approach can then be applied iteratively. This results in breaking
the problem down into finding the solutions to many small eigenvalue problems,
which can be efficiently solved using standard methods.
Since this methodology does not rely on the quasi steady-state assumption,
the effective dynamics that are approximated are highly accurate, and in the
case of systems with only monomolecular reactions, are exact. We will
demonstrate this with some numerics, and also use the effective generators to
sample paths of the slow variables which are conditioned on their endpoints, a
task which would be computationally intractable for the generator of the full
system.Comment: 31 pages, 7 figure
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