the reaction rates involved. To this end, after a brief introduction, we describe the
tools that we use and the modeling methodology that we follow and provide a short
tutorial and input files for the microkinetic simulator that we normally use (available
free of charge). Finally, we analyze two case examples to show the level of insight
and prediction power attainable with this DFT-based microkinetic modeling
methodology.
In an elementary reaction, the reactants form products in a single step (without
intermediates) with a single transition state (or with no barrier). Unlike empirical
laws, which encapsulate several unknown reaction steps, elementary reactions have
the advantage that they can be calculated directly by QM methods. One simply has to
minimize the reactants and products and find the transition state, using algorithms
implemented in QM software. Semiempirical and QM/MM can also be, for some
systems, good methods to achieve computational rates. Indeed, it would always be
desirable to describe our reaction by means of elementary reactions. The trade-off is
twofold:
1. The difficulty to figure out a plausible mechanism
2. The computation time and effort to find the transition states
There are some theoretical and experimental tools that can help our chemical
intuition and expertise in the forefront task of finding a plausible mechanism.
Regarding theoretical tools, an update on automated reaction profile search methods
can be found in chapter “Artificial Force-Induced Reaction Method for Systematic
Elucidation of Mechanism and Selectivity in Organometallic Reactions” of this
book. Those methods can provide candidate mechanisms as well as the involved
transition-state structures. Ideally, this would be the preferred approach. However,
while they are being used routinely for the second task, the computational cost of
automated search of reaction profiles can be prohibitive for the size of molecules
involved in most cases of practical interest. In those cases, it is necessary to rely on
chemical knowledge and intuition and previous modeling expertise. Fortunately,
there are some experimental tools that can provide guidance even in the cases that are
not amenable to be treated by theoretical search methods. In this regard, we briefly
present the reaction progress kinetic analysis (RPKA) methodology [2], as an
experimental tool for identifying the mechanisms, and then the theoretical tools
that we routinely employ to obtain the computationally derived rate constants.
2 Identification of Mechanisms: Experimental Tools
As a notable experimental tool, the reaction progress kinetic analysis (RPKA)
[11, 12] methodology “employs in situ measurements and simple manipulations to
construct a series of graphical rate equations that enable analysis of the reaction to
be accomplished from a minimal number of experiments. Such an analysis helps to
describe the driving forces of a reaction and may be used to help distinguish between
different proposed mechanistic models” [11]. Instead of using only single yields and
conversion values, “monitoring the time evolution of the reaction can yield
84
M. Jaraíz
tools that we use and the modeling methodology that we follow and provide a short
tutorial and input files for the microkinetic simulator that we normally use (available
free of charge). Finally, we analyze two case examples to show the level of insight
and prediction power attainable with this DFT-based microkinetic modeling
methodology.
In an elementary reaction, the reactants form products in a single step (without
intermediates) with a single transition state (or with no barrier). Unlike empirical
laws, which encapsulate several unknown reaction steps, elementary reactions have
the advantage that they can be calculated directly by QM methods. One simply has to
minimize the reactants and products and find the transition state, using algorithms
implemented in QM software. Semiempirical and QM/MM can also be, for some
systems, good methods to achieve computational rates. Indeed, it would always be
desirable to describe our reaction by means of elementary reactions. The trade-off is
twofold:
1. The difficulty to figure out a plausible mechanism
2. The computation time and effort to find the transition states
There are some theoretical and experimental tools that can help our chemical
intuition and expertise in the forefront task of finding a plausible mechanism.
Regarding theoretical tools, an update on automated reaction profile search methods
can be found in chapter “Artificial Force-Induced Reaction Method for Systematic
Elucidation of Mechanism and Selectivity in Organometallic Reactions” of this
book. Those methods can provide candidate mechanisms as well as the involved
transition-state structures. Ideally, this would be the preferred approach. However,
while they are being used routinely for the second task, the computational cost of
automated search of reaction profiles can be prohibitive for the size of molecules
involved in most cases of practical interest. In those cases, it is necessary to rely on
chemical knowledge and intuition and previous modeling expertise. Fortunately,
there are some experimental tools that can provide guidance even in the cases that are
not amenable to be treated by theoretical search methods. In this regard, we briefly
present the reaction progress kinetic analysis (RPKA) methodology [2], as an
experimental tool for identifying the mechanisms, and then the theoretical tools
that we routinely employ to obtain the computationally derived rate constants.
2 Identification of Mechanisms: Experimental Tools
As a notable experimental tool, the reaction progress kinetic analysis (RPKA)
[11, 12] methodology “employs in situ measurements and simple manipulations to
construct a series of graphical rate equations that enable analysis of the reaction to
be accomplished from a minimal number of experiments. Such an analysis helps to
describe the driving forces of a reaction and may be used to help distinguish between
different proposed mechanistic models” [11]. Instead of using only single yields and
conversion values, “monitoring the time evolution of the reaction can yield
84
M. Jaraíz