Molecular modeling problem solving

In the near future, the main concerns of CAPE will probably focus on issues related to product design (molecular modeling for solving function-property-composition problems), supply chain management (cost reduction of raw materials, effective use of energy and its new sources), and life cycle assessment (mitigation of climate change, process sustainability). 

The visuahzation of hundreds or thousands of connected atoms, which are found in biological macromolecules, is no longer reasonable with the molecular models described above because too much detail would be shown. First of aU the models become vague if there are more than a few himdied atoms. This problem can be solved with some simplified models, which serve primarily to represent the secondary structure of the protein or nucleic acid backbone [201]. (Compare the balls and sticks model (Figure 2-124a) and the backbone representation (Figure 2-124b) of lysozyme.)... 

Linking this molecular model to observed bulk fluid PVT-composition behavior requires a calculation of the number of possible configurations (microstmctures) of a mixture. There is no exact method available to solve this combinatorial problem (28). ASOG assumes the athermal (no heat of mixing) FIory-Huggins equation for this purpose (118,170,171). UNIQUAC claims to have a formula that avoids this assumption, although some aspects of athermal mixing are still present in the model. 

For the solubility of TPA in prepolymer, no data are available and the polymer-solvent interaction parameter X of the Flory-Huggins relationship is not accurately known. No experimental data are available for the vapour pressures of dimer or trimer. The published values for the diffusion coefficient of EG in solid and molten PET vary by orders of magnitude. For the diffusion of water, acetaldehyde and DEG in polymer, no reliable data are available. It is not even agreed upon if the mutual diffusion coefficients depend on the polymer molecular weight or on the melt viscosity, and if they are linear or exponential functions of temperature. Molecular modelling, accompanied by the rapid growth of computer performance, will hopefully help to solve this problem in the near future. The mass-transfer mechanisms for by-products in solid PET are not established, and the dependency of the solid-state polycondensation rate on crystallinity is still a matter of assumptions. 

The examples shown above clearly demonstrate the potential of molecular modeling for structure analysis. Modeling helps to solve difficult structural problems and adds new information concerning long range intermolecular interactions. 

There are many situations where direct structure determination is impossible or not reliable due to a variety of reasons as discussed above. Thus, molecular modeling is more than welcome to intervene in solving the problems. 

A common alternative is to synthesize approximate state functions by linear combination of algebraic forms that resemble hydrogenic wave functions. Another strategy is to solve one-particle problems on assuming model potentials parametrically related to molecular size. This approach, known as free-electron simulation, is widely used in solid-state and semiconductor physics. It is the quantum-mechanical extension of the classic (1900) Drude model that pictures a metal as a regular array of cations, immersed in a sea of electrons. Another way to deal with problems of chemical interaction is to describe them as quantum effects, presumably too subtle for the ininitiated to ponder. Two prime examples are, the so-called dispersion interaction that explains van der Waals attraction, and Born repulsion, assumed to occur in ionic crystals. Most chemists are in fact sufficiently intimidated by such claims to consider the problem solved, although not understood. 

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