Molecule, design problem formulation

We start with a definition of the problem and based on this, we identify the candidates (such as, molecules, mixtures and formulations) through expert knowledge, database search, model-based search, or a combination of all. The next step is to perform experiments and/or model-based simulations (of product behavior) to identify a feasible set of candidates. At this stage, issues related to process design are introduced and a process-product match is obtained. The final test is related to product quality and performance verification. Other features, such as life cycle assessment could also be introduced at this stage. 

Component failure is so crucial that Caterpillar does not trust any other company to make these rubber products—not even Goodyear or Firestone. Caterpillar makes its own rubber component formulations. Rubber component failure is a multilevel issue performance depends on the rubber parts, which depend on the rubber component-based materials. This, in turn, depends on the failure mechanics properties of these materials, which are affected by rubber curing chemistry. In the end, the design- and manufacturing-related issues depend on quantum chemistry of sulfur links. This is another problem in which the transformation process goes from molecules to materials to market and has the proverbial brick wall in between. 

One other problem is combinatorially large search spaces. There are 100 million potential candidates for rubber components formulation that are possible. Some other examples we have worked at Purdue have involved 1020 to 1030 different molecules. Another issue is that typically there are limited and uncertain data. Most often, combinatorial chemistry approaches do not succeed in these cases because obtaining the results is time and labor intensive. Fuel additives design, for example, requires dismantling the engine for every test of a new formula. 

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