55,680 research outputs found
Mechanism Deduction from Noisy Chemical Reaction Networks
We introduce KiNetX, a fully automated meta-algorithm for the kinetic
analysis of complex chemical reaction networks derived from semi-accurate but
efficient electronic structure calculations. It is designed to (i) accelerate
the automated exploration of such networks, and (ii) cope with model-inherent
errors in electronic structure calculations on elementary reaction steps. We
developed and implemented KiNetX to possess three features. First, KiNetX
evaluates the kinetic relevance of every species in a (yet incomplete) reaction
network to confine the search for new elementary reaction steps only to those
species that are considered possibly relevant. Second, KiNetX identifies and
eliminates all kinetically irrelevant species and elementary reactions to
reduce a complex network graph to a comprehensible mechanism. Third, KiNetX
estimates the sensitivity of species concentrations toward changes in
individual rate constants (derived from relative free energies), which allows
us to systematically select the most efficient electronic structure model for
each elementary reaction given a predefined accuracy. The novelty of KiNetX
consists in the rigorous propagation of correlated free-energy uncertainty
through all steps of our kinetic analyis. To examine the performance of KiNetX,
we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction
networks by encoding chemical logic into their underlying graph structure.
AutoNetGen allows us to consider a vast number of distinct chemistry-like
scenarios and, hence, to discuss assess the importance of rigorous uncertainty
propagation in a statistical context. Our results reveal that KiNetX reliably
supports the deduction of product ratios, dominant reaction pathways, and
possibly other network properties from semi-accurate electronic structure data.Comment: 36 pages, 4 figures, 2 table
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
Maximum Margin Clustering for State Decomposition of Metastable Systems
When studying a metastable dynamical system, a prime concern is how to
decompose the phase space into a set of metastable states. Unfortunately, the
metastable state decomposition based on simulation or experimental data is
still a challenge. The most popular and simplest approach is geometric
clustering which is developed based on the classical clustering technique.
However, the prerequisites of this approach are: (1) data are obtained from
simulations or experiments which are in global equilibrium and (2) the
coordinate system is appropriately selected. Recently, the kinetic clustering
approach based on phase space discretization and transition probability
estimation has drawn much attention due to its applicability to more general
cases, but the choice of discretization policy is a difficult task. In this
paper, a new decomposition method designated as maximum margin metastable
clustering is proposed, which converts the problem of metastable state
decomposition to a semi-supervised learning problem so that the large margin
technique can be utilized to search for the optimal decomposition without phase
space discretization. Moreover, several simulation examples are given to
illustrate the effectiveness of the proposed method
Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments
In many chemical and biological applications, systems of differential
equations containing unknown parameters are used to explain empirical
observations and experimental data. The DEs are typically nonlinear and
difficult to analyze, requiring numerical methods to approximate the solutions.
Compounding this difficulty are the unknown parameters in the DE system, which
must be given specific numerical values in order for simulations to be run.
Estrogen receptor protein dimerization is used as an example to demonstrate
model construction, reduction, simulation, and parameter estimation.
Mathematical, computational, and statistical methods are applied to empirical
data to deduce kinetic parameter estimates and guide decisions regarding future
experiments and modeling. The process demonstrated serves as a pedagogical
example of quantitative methods being used to extract parameter values from
biochemical data models.Comment: 23 pages, 9 figures, to be published in SIAM Revie
Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification
A massively parallel method to build large transition rate matrices from
temperature accelerated molecular dynamics trajectories is presented. Bayesian
Markov model analysis is used to estimate the expected residence time in the
known state space, providing crucial uncertainty quantification for higher
scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The
estimators are additionally used to optimize where exploration is performed and
the degree of temperature ac- celeration on the fly, giving an autonomous,
optimal procedure to explore the state space of complex systems. The method is
tested against exactly solvable models and used to explore the dynamics of C15
interstitial defects in iron. Our uncertainty quantification scheme allows for
accurate modeling of the evolution of these defects over timescales of several
seconds.Comment: 14 pages, 7 figure
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
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