Structure simulation models using quantum mechanical method

With respect to the various spectroscopic methods not discussed so far there are four methodologically different concepts of molecular modeling (i) quantum-mechanical methods which result structural and electronic information (ii) MM-based structural modeling, followed by a single-point MO-based computation of the electronic properties (iii) spectra simulation with given electronic parameters in cases, where structural parameters (e.g., distances such as used in protein modeling, see above) are involved in the simulation (iv) structure property correlations. The latter approach will be discussed briefly at the end of this Section (redox potentials) and in Section 4. 

First principles approaches are important as they avoid many of the pitfalls associated with using parameterized descriptions of the interatomic interactions. Additionally, simulation of chemical reactivity, reactions and reaction kinetics really requires electronic structure calculations [108]. However, such calculations were traditionally limited in applicability to rather simplistic models. Developments in density functional theory are now broadening the scope of what is viable. Car-Parrinello first principles molecular dynamics are now being applied to real zeolite models [109,110], and the combined use of classical and quantum mechanical methods allows quantum chemical methods to be applied to cluster models embedded in a simpler description of the zeoUte cluster environment [105,111]. 

Molecular dynamic simulation methods, in addition to being essential for interpreting NMR data at the atomic level, also augment experimental studies in a number of other ways [101] modeling techniques can (i) yield structural information where experimental data has not yet been acquired, (ii) expand on experimental data through simulations that yield dynamic trajectories whose analysis provides unique information on lesion mobility, and (iii) provide thermodynamic insights by ensemble analysis using statistical mechanical methods. Furthermore, reaction mechanisms can now be determined with some confidence by combined quantum mechanical and molecular mechanical methods [104, 105],... 

The disparate time and length scales that control heterogeneous catalytic processes make it essentially impossible to arrive at a single method to treat the complex structural behavior, reactivity and dynamics. Instead, a hierarchy of methods have been developed which can can be used to model different time and length scales. Molecular modeling of catalysis covers a broad spectrum of different methods but can be roughly categorized into either quantum-mechanical methods which track the electronic structure or molecular simulations which track the atomic structme (see the Appendix). 

Much like the choice ot the empirical potential functional form discussed in Sect. 2 above, the choice of the quantum mechanical method and model is a compromise between speed of evaluation and accuracy. The most rigorous approach to evaluating these energy and force terms would be to use ab initio quantum chemical methods with large basis sets and correlation corrections beyond the Hartree Fock level. Clearly, this is currently not a feasible approach because of the computional demands such as model places on a single energy evaluation, not to mention the iterative evaluation over thousands of structures (timesteps) of a dynamics simulation. 

The advent of high-speed computers, availability of sophisticated algorithms, and state-of-the-art computer graphics have made plausible the use of computationally intensive methods such as quantum mechanics, molecular mechanics, and molecular dynamics simulations to determine those physical and structural properties most commonly involved in molecular processes. The power of molecular modeling rests solidly on a variety of well-established scientific disciplines including computer science, theoretical chemistry, biochemistry, and biophysics. Molecular modeling has become an indispensable complementary tool for most experimental scientific research. 

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