Computational thermochemistry computer models

A. K. Cheetham and J. D. Gale, in Computer Modeling of Structure and Reactivity of Zeolites, C. R. A. Catlow, Ed., Academic Press, London, 1992, pp. 63-78. Computer Simulation of the Structure, Thermochemistry and Dynamics of Adsorbed Molecules in Zeolites and Related Catalysts. 

In order to evaluate the isomerization rate constant (via the second channel), a kinetics scheme containing all the four elementary steps, forward and backward, has been constructed. Their rate constants were calculated from the AS and A for each elementary step. Together with the thermochemistry of the reactant, product, and all the intermediates on the surface, the scheme could be computer modeled to evaluate the product yields, and then the final rate constant for the reactant —> product reaction. Figure 6.36 shows a comparison between the results of the experiment and the calculations. The difference is about a factor of 3 that corresponds to a disagreement of approximately 2.5 kcal/mol. 

As stated above, the thermochemistry of free radicals can also be estimated by the group additivity method, if group values are available. With the exception of a few cases reported in Benson (1976), however, such information presently does not exist. Therefore, we rely on the model compound approach (for S and Cp) and bond dissociation energy (BDE) considerations and computational quantum mechanics for the determination of the heats of formation of radicals. 

In summary, bond and group additivity rules, as well as the model compound approach, in conjunction with statistical mechanics, represent useful tools for the estimation of thermochemical properties. However, their utility for the determination of thermochemistry of new classes of compounds is limited, especially with regard to the determination of Aiff. For new classes of compounds, we must resort to experiments, as well as to computational quantum mechanical methods. 

G3 is a recipe involving a variety of different models with the purpose of providing accurate thermochemical data. Original reference (a) L. A. Curtiss, K. Raghavachari, PC. Redfem, V. Rassolov and J.A. Pople, J. Chem. Phys., 109, 7764 (1998). For an up-to-date, on-line source of G3 data see (b) L A. Curtiss, Computational Thermochemistry, chemistry.anl.gov/compmat/ comptherm.htm ... 

A modification of G2 by Pople and co-workers was deemed sufficiently comprehensive tliat it is known simply as G3, and its steps are also outlined in Table 7.6. G3 is more accurate titan G2, witli an error for the 148-molecule heat-of-formation test set of 0.9 kcal mol . It is also more efficient, typically being about twice as fast. A particular improvement of G3 over G2 is associated with improved basis sets for tlie third-row nontransition elements (Curtiss et al. 2001). As with G2, a number of minor to major variations of G3 have been proposed to either improve its efficiency or increase its accuracy over a smaller subset of chemical space, e.g., the G3-RAD method of Henry, Sullivan, and Radom (2003) for particular application to radical thermochemistry, the G3(MP2) model of Curtiss et al. (1999), which reduces computational cost by computing basis-set-extension corrections at the MP2 level instead of the MP4 level, and the G3B3 model of Baboul et al. (1999), which employs B3LYP structures and frequencies. 

The concomitant advances in theoretical methodologies and algorithms have also played a vital role in increasing computational capabilities for theoretical thermochemistry. These advances include (1) new methods for accurate treatment of electron correlation in molecules and atoms such as coupled cluster and quadratic configuration interaction methods, (2) new basis sets such as the correlation consistent basis sets, and (3) development of model chemistry ... 

Many of the technical issues involved in computer-aided model-construction have previously been reviewed by Tomlin et al. (1997). Several researchers, most notably Bozzelli, have extended Benson s method for estimating molecular thermochemistry using quantum chemistry (Lay and Bozzelli, 1997a, b Lay et al., 1995). Sumathi and Green (2002) have discussed how quantum chemistry can supplement experiments in developing rate estimation. Matheu et al. have shown how to automate the computation of rates of chemically-activated (pressure-dependent) reactions (Matheu, 2003 Matheu et al., 2003a, b). Here we focus on a few issues which have not been so thoroughly discussed in the literature ... 

More importantly, as written, the partial derivative implies that a single rate constant is to be varied, holding all the others constant, and indeed this is the way it is implemented in many sensitivity analysis routines. The index y runs out to 2Areactions because each reaction has two rate constants, one for the forward direction, and one for the reverse. However, in order for the model to remain consistent with the laws of thermodynamics, the rate constant for the reverse of reaction y must vary simultaneously with the forward reaction, since the two rate constants must maintain a detailed-balance ratio related to the AGreaction, Eq. (2). This can be assured by specifying that the partial derivative is taken only for the forward reaction, while holding the thermochemistry fixed. Note that this also cuts the number of partial derivatives to be computed in half. These sensitivities should then be supplemented with sensitivities to the individual species thermochemistry as in Eq. (25) overall the number of partial derivatives to be computed per model prediction M, is... 

There is an obvious discrepancy between the experimental solution thermochemistry, which seems to support the expanded octet model, and recent computational results, which are more consistent with the 3c-4e... 

The hrst hve chapters (Part 1) present an overview of some methods that have been used in the recent hterature to calculate rate constants and the associated case studies. The main topics covered in this part include thermochemistry and kinetics, computational chemistry and kinetics, quantum instanton, kinetic calculations in liquid solutions, and new applications of density functional theory in kinetic calculations. The remaining hve chapters (Part II) are focused on apphcations even though methodologies are discussed. The topics in the second part include the kinetics of molecules relevant to combustion processes, intermolecular electron transfer reactivity of organic compounds, lignin model compounds, and coal model compounds in addition to free radical polymerization. 

Calculations of electrode potentials for metal complexes in solution and metalloproteins have progressed remarkably with increasing computational power and the greater sophistication of combined quantum/classical models. It is clear that the portion of the problem accessible to quantum methods will continue to expand in the future, revealing even more subtle aspects of the relation of redox thermochemistry to structure, bonding, and solvation. 

Advances in computational chemistry and molecular simulation have also reached the stage whereby they can be used to develop more advanced and robust kinetic models for catalytic systems. First-principle quantum chemical methods, for example, are being used to routinely calculate thermochemistry and kinetics for gas phase chemistry with accuracies on the order of... 

R.S. Drago, T.R. Cundari, Electrostatic-Covalent Model Parameters for Molecular Modeling, in K.K. Irikura, D.J. Frurip (eds.). Computational Thermochemistry Prediction and Estimation of Molecular Thermodynamics, ACS Symposium Series 677. American Chemical Society, Washington, DC, 1998. 

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