Cumulative properties molecular weight

Figure 1 Is a flow sheet showing some significant aspects of the Iterative analysis. The first step In the program Is to Input data for about 50 physical, chemical and kinetic properties of the reactants. Each loop of this analysis Is conducted at a specified solution temperature T K. Some of the variables computed In each loop are the monomer conversion, polymer concentration, monomer and polymer volume fractions, effective polymer molecular weight, cumulative number average molecular weight, cumulative weight average molecular weight, solution viscosity, polymerization rate, ratio of polymerization rates between the current and previous steps, the total pressure and the partial pressures of the monomer, the solvent, and the nitrogen.

Another sampling effect which deserves mention is that since the molecular weight distribution shifl towards higher molecular weights with conversion, a slice will not in general contain proportionate amounts of polymer from all conversions. This dufting can be accounted for in the theoretical predictions by incorporating it into cumulation of the instantaneous property distributions (e.g. Equation 8). 

The differences can also be seen in Figure 15.4, which compares cumulative distributions of these properties between the Gasteiger and Roche datasets. Many of the literature compounds are very simple, with low molecular weight, few polar atoms, and few functional groups. They have often been included in solubility datasets because they are well characterized and because accurate solubility data are available for them, rather than because they are druglike. The inclusion of many such simple compounds in a training set for a solubility prediction tool may focus the tool on an area of chemistry space that is not well populated with druglike molecules and may make the tool less useful for the prediction of the solubility... 

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