Binary Comparison

An entry in a data set table can be directly compared via root mean square or correlation coefficient with one of the default binary databases. A new window opens that contains two areas with three-dimensional molecule models The left one displays the original molecule of the selected data set entry. The window shows the molecule and a match list containing the most similar entries of the database according to the selected similarity criterion (descriptor) with their sequential number, the names, and the calculated similarity measures. 

Another type of binary file is the binary database. This type is similar to the binary molecule set but is especially designed for fast searches of similar descriptors and the retrieval of the corresponding molecule. An example of the use of binary databases is the prediction of a descriptor from the neiu al network after a reverse training. 

A correlation matrix of all the descriptors in a data set can be calculated, including a regression chart displaying the regression lines of the entries selected in the correlation matrix and a descriptor chart that shows both descriptors in comparison to the ASD. 

The regression chart contains two axes one for each descriptor. The graph consists of correlation points (the probability values of each vector component in relation to the second descriptor), as well as two regression lines one for each descriptor understood as an independent variable (b y and by, respectively). 

By clicking either a correlation point or a descriptor at a certain position a list of the corresponding atom pairs is displayed in the descriptor chart. The correlation matrix provided its own context menu to find the best and worst correlation coefficient without searching the complete matrix. 

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A further insight is that the best workflow depends on a combination of factors that can in many cases be expressed in closed mathematical form, allowing very rapid graphical feedback to users of what then becomes a visualization rather than a stochastic simulation tool. This particular approach is effective for simple binary comparisons of methods (e.g., use of in vitro alone vs. in silico as prefilter to in vitro). It can also be extended to evaluation of conditional sequencing for groups of compounds, using an extension of the sentinel approach [24]. 

Table 3 shows results obtained from a five-component, isothermal flash calculation. In this system there are two condensable components (acetone and benzene) and three noncondensable components (hydrogen, carbon monoxide, and methane). Henry s constants for each of the noncondensables were obtained from Equations (18-22) the simplifying assumption for dilute solutions [Equation (17)] was also used for each of the noncondensables. Activity coefficients for both condensable components were calculated with the UNIQUAC equation. For that calculation, all liquid-phase composition variables are on a solute-free basis the only required binary parameters are those for the acetone-benzene system. While no experimental data are available for comparison, the calculated results are probably reliable because all simplifying assumptions are reasonable the... 

Figure C2.16.3. A plot of tire energy gap and lattice constant for tire most common III-V compound semiconductors. All tire materials shown have cubic (zincblende) stmcture. Elemental semiconductors. Si and Ge, are included for comparison. The lines connecting binary semiconductors indicate possible ternary compounds witli direct gaps. Dashed lines near GaP represent indirect gap regions. The line from InP to a point marked represents tire quaternary compound InGaAsP, lattice matched to InP.

The most recendy developed model is called UNIQUAC (21). Comparisons of measured VLE and predicted values from the Van Laar, Wilson, NRTL, and UNIQUAC models, as well as an older model, are available (3,22). Thousands of comparisons have been made, and Reference 3, which covers the Dortmund Data Base, available for purchase and use with standard computers, should be consulted by anyone considering the measurement or prediction of VLE. The predictive VLE models can be accommodated to multicomponent systems through the use of certain combining rules. These rules require the determination of parameters for all possible binary pairs in the multicomponent mixture. It is possible to use more than one model in determining binary pair data for a given mixture (23). 

Adsorption of hard sphere fluid mixtures in disordered hard sphere matrices has not been studied profoundly and the accuracy of the ROZ-type theory in the description of the structure and thermodynamics of simple mixtures is difficult to discuss. Adsorption of mixtures consisting of argon with ethane and methane in a matrix mimicking silica xerogel has been simulated by Kaminsky and Monson [42,43] in the framework of the Lennard-Jones model. A comparison with experimentally measured properties has also been performed. However, we are not aware of similar studies for simpler hard sphere mixtures, but the work from our laboratory has focused on a two-dimensional partly quenched model of hard discs [44]. That makes it impossible to judge the accuracy of theoretical approaches even for simple binary mixtures in disordered microporous media. 

Table 4.1-1 Comparison of the neutron scattering cross-sections for Li and Li with all the other atoms present in a binary mixture of LiSCN with AICI3.

The dilated van Laar model is readily generalized to the multicomponent case, as discussed in detail elsewhere (C3, C4). The important technical advantage of the generalization is that it permits good estimates to be made of multicomponent phase behavior using only experimental data obtained for binary systems. For example, Fig. 14 presents a comparison of calculated and observed -factors for the methane-propane-n-pentane system at conditions close to the critical.7... 

This development was technologically successful and E-plastomers are widely recognized to be very effective impact modifiers for iPP. In initial experiments, binary blends of iPP were compounded with EPDM, E-plastomers, or SEES as the elastomeric phase. In comparison to the known modifiers, such as EPDM or SEES, the binary blends with E-plastomers have properties which strongly depend on the amount and the identity of the E-plastomer. Thus, the addition of... 

A general method has been developed for the estimation of model parameters from experimental observations when the model relating the parameters and input variables to the output responses is a Monte Carlo simulation. The method provides point estimates as well as joint probability regions of the parameters. In comparison to methods based on analytical models, this approach can prove to be more flexible and gives the investigator a more quantitative insight into the effects of parameter values on the model. The parameter estimation technique has been applied to three examples in polymer science, all of which concern sequence distributions in polymer chains. The first is the estimation of binary reactivity ratios for the terminal or Mayo-Lewis copolymerization model from both composition and sequence distribution data. Next a procedure for discriminating between the penultimate and the terminal copolymerization models on the basis of sequence distribution data is described. Finally, the estimation of a parameter required to model the epimerization of isotactic polystyrene is discussed. 

On the other hand, very few ncdels for nulticonponent systans have been reported in the literature. Apart from models for binary systems, usually restricted to "zero-one" systans (5) (6), the most detailed model of this type has been proposed by Hamielec et al. (7), with reference to batch, semibatch and continuous emilsion polymerization reactors. Notably, besides the usual kinetic informations (nonomer, conversion, PSD), the model allows for the evaluation of IWD, long and short chain brandling frequencies and gel content. Comparisons between model predictions and experimental data are limited to tulK and solution binary pwlymerization systems. 

The HDS activity of CoSx-MoSx/NaY is shown in Fig.3 as a function of the Co/Mo atomic ratio. The Mo content in the catalyst was kept constant at the saturation value for a Mo(CO)j adsorption in NaY, 2.1Mo/SC. The HDS activity increased as the Co/Mo ratio increased up to about unity, followed by an activity decrease at a further addition of Co. The simple sum of the activities of the corresponding CoSx/NaY and MoSx/NaY is presented in Fig.3 for comparison. It is evident that the activity of the Co-Mo binary sulfide catalyst is considerably higher than the simple sum of the activities of the composite sulfides, indicating... 

Figure 7.4 shows such functions for binary solutions of a number of strong electrolytes and for the purposes of comparison, for solutions of certain nonelectrolytes (/ ). We can see that in electrolyte solutions the values of the activity coefficients vary within much wider limits than in solutions of nonelectrolytes. In dilute electrolyte solutions the values of/+ always decrease with increasing concentration. For... 

FIGURE 2.18 Comparison of the concentration profiles of 2-phenylbutyric acid (dashed line) and benzophenone (thin solid line) developed as single analytes and as a binary mixture (bold solid line) concentration of 2-phenylbutyric acid in the sample was 1.25 mol 1 and that of benzophenone was 0.10 mol FI Microcrystalline cellulose was used as stationary phase and decalin as mobile phase [26]. 

The experiments showing the influence of lateral interaction on coelution of the two species were discussed in Subsection 2.4.2. Figure 2.18 and Figure 2.19 give a comparison of single profiles of acid and ketone or of alcohol and ketone with those attained for the binary mixture. Very similar peak profiles can be obtained upon solving Equation 2.21 separately for the alcohol, acid, and ketone with isotherms (Equation 2.4 and Equation 2.7a), and for the binary mixture with the isotherms (Equation 2.9 and Equation 2.10). 

Golshan-Shirazi, S. and Guiochon, G., Comparison between experimental and theoretical band profiles in nonlinear liquid chromatography with a binary mobile phase, Anal. Chem., 61, 1276, 1989. 

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