3,825 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
Automated transition state theory calculations for high-throughput kinetics
A scarcity of known chemical kinetic parameters leads to the use of many
reaction rate estimates, which are not always sufficiently accurate, in the
construction of detailed kinetic models. To reduce the reliance on these
estimates and improve the accuracy of predictive kinetic models, we have
developed a high-throughput, fully automated, reaction rate calculation method,
AutoTST. The algorithm integrates automated saddle-point geometry search
methods and a canonical transition state theory kinetics calculator. The
automatically calculated reaction rates compare favorably to existing estimated
rates. Comparison against high level theoretical calculations show the new
automated method performs better than rate estimates when the estimate is made
by a poor analogy. The method will improve by accounting for internal rotor
contributions and by improving methods to determine molecular symmetry.Comment: 29 pages, 8 figure
Navigating chemical reaction space with a steering wheel
Autonomous reaction network exploration algorithms offer a systematic
approach to explore mechanisms of complex chemical processes. However, the
resulting reaction networks are so vast that an exploration of all potentially
accessible intermediates is computationally too demanding. This renders
brute-force explorations unfeasible, while explorations with completely
pre-defined intermediates or hard-wired chemical constraints, such as
element-specific coordination numbers, are not flexible enough for complex
chemical systems. Here, we introduce a Steering Wheel to guide an otherwise
unbiased automated exploration. The Steering Wheel algorithm is intuitive,
generally applicable, and enables one to focus on specific regions of an
emerging network. It also allows for guiding automated data generation in the
context of mechanism exploration, catalyst design, and other chemical
optimization challenges. The algorithm is demonstrated for reaction mechanism
elucidation of transition metal catalysts. We highlight how to explore
catalytic cycles in a systematic and reproducible way. The exploration
objectives are fully adjustable, allowing one to harness the Steering Wheel for
both structure-specific (accurate) calculations as well as for broad
high-throughput screening of possible reaction intermediates.Comment: 40 pages, 10 figures, 1 tabl
Automated reaction mechanism generation : improving accuracy and broadening scope
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 169-186).Chemical kinetic modeling plays an important role in the study of reactive chemical systems. Thus, an automated means of constructing chemical kinetic models forms a useful tool in the engineering and science surrounding such systems. This document describes work to further develop one such tool, known as RMG (Reaction Mechanism Generator). Focus is placed on improving the accuracy of parameter estimation in the mechanism generation process and expanding the scope of applicability of the tool. In particular, effort has targeted the generation and use of explicit three-dimensional molecular structures for chemical species considered during reaction mechanism generation. This work has resulted in the generation of a software system integrated with RMG that can automatically generate and use such structures with quantum chemistry or force field codes to obtain more reliable thermochemistry estimates for cyclic structures without human intervention. Ultimately, the result of these updates is improved usefulness and reliability of the software system as a predictive tool. An application of the tool to the high temperature oxidation of JP-10, a jet fuel often used in military applications, is described. Using the newly refined RMG system, a detailed chemical kinetic model was constructed for this system. The resulting model represents a significant improvement upon existing work for JP- 10 oxidation by capturing detailed chemistry for this system. Simulations with this model have been found to produce results for ignition delay and product distribution that compare favorably with experimental results. The successful application of the refined RMG software system to this system demonstrates the practical utility of these updates.by Gregory Russell Magoon.Ph.D
Universal QM/MM Approaches for General Nanoscale Applications
Hybrid quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one
to address chemical phenomena in complex molecular environments. However, they
are tedious to construct and they usually require significant manual
preprocessing and expertise. As a result, these models may not be easily
transferable to new application areas and the many parameters are not easy to
adjust to reference data that are typically scarce. Therefore, it has been
difficult to devise automated procedures of controllable accuracy, which makes
such type of modelling far from being standardized or of black-box type.
Although diverse best-practice protocols have been set up for the construction
of individual components of a QM/MM model (e.g., the MM potential, the type of
embedding, the choice of the QM region), no automated procedures are available
for all steps of the QM/MM model construction. Here, we review the state of the
art of QM/MM modeling with a focus on automation. We elaborate on the MM model
parametrization, on atom-economical physically-motivated QM region selection,
and on embedding schemes that incorporate mutual polarization as critical
components of the QM/MM model. In view of the broad scope of the field, we
mostly restrict the discussion to methodologies that build de novo models based
on first-principles data, on uncertainty quantification, and on error
mitigation with a high potential for automation. Ultimately, it is desirable to
be able to set up reliable QM/MM models in a fast and efficient automated way
without being constrained by some specific chemical or technical limitations.Comment: 54 pages, 3 figures, 1 tabl
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
Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods
We present a simple protocol which allows fully automated discovery of
elementary chemical reaction steps using in cooperation single- and
double-ended transition-state optimization algorithms - the freezing string and
Berny optimization methods, respectively. To demonstrate the utility of the
proposed approach, the reactivity of several systems of combustion and
atmospheric chemistry importance is investigated. The proposed algorithm
allowed us to detect without any human intervention not only "known" reaction
pathways, manually detected in the previous studies, but also new, previously
"unknown", reaction pathways which involve significant atom rearrangements. We
believe that applying such a systematic approach to elementary reaction path
finding will greatly accelerate the possibility of discovery of new chemistry
and will lead to more accurate computer simulations of various chemical
processes
Molecular propensity as a driver for explorative reactivity studies
Quantum chemical studies of reactivity involve calculations on a large number
of molecular structures and comparison of their energies. Already the set-up of
these calculations limits the scope of the results that one will obtain,
because several system-specific variables such as the charge and spin need to
be set prior to the calculation. For a reliable exploration of reaction
mechanisms, a considerable number of calculations with varying global
parameters must be taken into account, or important facts about the reactivity
of the system under consideration can go undetected. For example, one could
miss crossings of potential energy surfaces for different spin states or might
not note that a molecule is prone to oxidation. Here, we introduce the concept
of molecular propensity to account for the predisposition of a molecular system
to react across different electronic states in certain nuclear configurations.
Within our real-time quantum chemistry framework, we developed an algorithm
that allows us to be alerted to such a propensity of a system under
consideration.Comment: 10 pages, 7 figure
Kinetic model construction using chemoinformatics
Kinetic models of chemical processes not only provide an alternative to costly experiments; they also have the potential to accelerate the pace of innovation in developing new chemical processes or in improving existing ones. Kinetic models are most powerful when they reflect the underlying chemistry by incorporating elementary pathways between individual molecules. The downside of this high level of detail is that the complexity and size of the models also steadily increase, such that the models eventually become too difficult to be manually constructed. Instead, computers are programmed to automate the construction of these models, and make use of graph theory to translate chemical entities such as molecules and reactions into computer-understandable representations.
This work studies the use of automated methods to construct kinetic models. More particularly, the need to account for the three-dimensional arrangement of atoms in molecules and reactions of kinetic models is investigated and illustrated by two case studies. First of all, the thermal rearrangement of two monoterpenoids, cis- and trans-2-pinanol, is studied. A kinetic model that accounts for the differences in reactivity and selectivity of both pinanol diastereomers is proposed. Secondly, a kinetic model for the pyrolysis of the fuel “JP-10” is constructed and highlights the use of state-of-the-art techniques for the automated estimation of thermochemistry of polycyclic molecules.
A new code is developed for the automated construction of kinetic models and takes advantage of the advances made in the field of chemo-informatics to tackle fundamental issues of previous approaches. Novel algorithms are developed for three important aspects of automated construction of kinetic models: the estimation of symmetry of molecules and reactions, the incorporation of stereochemistry in kinetic models, and the estimation of thermochemical and kinetic data using scalable structure-property methods. Finally, the application of the code is illustrated by the automated construction of a kinetic model for alkylsulfide pyrolysis
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