A process for the computation of physical and chemical properties of a proposed polymeric or copolymeric substance

Abstract: A process for the estimation of the physical properties of a proposed polymer comprises computing the chemical composition of the polymer from input data representing attributes of a monomer or monomers, computing properties of the polymer when three-dimensionally folded, computing a model of the folded molecular composition as a polymeric cluster and displaying the cluster on a graphics device.

Description: 1. Field Of The Invention The present invention relates to a computer-aided process for the estimation and development of proposed chemical polymeric or copolymeric materials or substances. In particular, this invention utilizes chemical kinetics, statistical thermodynamics and molecular mechanics, including experimental information, in the creation of a more exacting molecular model. 2. Description Of The Related Art Document IBM SYSTEMS JOURNAL vol. 28, no. 4, 1989, ARMONK, NEW YORK US pages 613 - 627 A.KOIDE 'DESIGNING MOLECULES AND CRYSTALS BY COMPUTER' gives an overview of computer-aided chemical design including a simulation system, an intelligent visual molecular model-building environment, and an interactive graphics system for the visualisation of molecular electronic structures. In the art of modeling polymers, a set of current techniques involve the use of molecular mechanics or molecular dynamics. Both methods have proven to be very useful in accounting for the conformational energetics and properties of polymer molecules. These methods are based on the premise that a molecule can be simulated by empirical transferable energy functions that represent bond stretching, bending, and twisting as well as more distant non-bonded or steric interactions. Electrostatic forces are included when appropriate. A stable conformation is found as a minima in the total energy function. This process has reached a high state of development and has had many successes. See Sorensen, R.A., Liau, W.B., Boyd, R.H., "Prediction of Polymer Structures and Properties", Macromolecules, 21 194-199, 1988. However, in contrast to the handful of stable minima found in small molecules, which are usually indicative of compounds used in the pharmaceutical or agricultural chemical industries, large polymer molecules have a great number of such "stable minima". Consequently, prior art modeling techniques were unable to effectively model large and complex polymers. Moreover, except for a few crystalline homopolymers, past polymer modeling approaches have always divorced the specific chemical molecular structure, such as atom connectivity and atomic coordinates of the polymer, from the chemical calculations. It is both important and desirable to include information about the chemical molecular structure because a large class of commercial polymers contain more than one monomer type. These polymers are often prepared with great regard for the intrinsic differences in the reaction kinetics of the various monomers. It is an object of the present invention to provide a computer-aided process for modeling polymers, which process would be effective for large molecules overcoming the "multiple stable minima" problem, as well as combining the specific chemical molecular structure in the calculation of physical and chemical properties. These large molecules are polymeric or copolymeric substances or materials which can be used in plastics, packaging materials, optical disk materials, barrier membranes, adhesives, viscosity improvers, dispersants, electronic chemicals, coatings, or synthetic biopolymers. Examples of plastics include polymer blend compatibilizers, high-temperature plastics, thermoplastic, elastomeric, amorphous, crystalline or liquid crystalline polymers, polymer blends, and barrier membranes for bio-separation materials. Figure 1 is a computer generated three-dimensionally folded full molecular structure using the process of the instant invention. All atoms are included for an atactic methyl methacrylate (MMA) chain, the main constituent in PLEXIGLAS®, a polymer sold by Rohm & Haas Company. Additional objects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the appended claims. SUMMARY OF THE INVENTION To achieve the foregoing objects, and in accordance with the purpose of the invention as embodied and broadly described herein, there is provided a process for the computation of physical properties of a proposed polymeric or copolymeric substance or material comprising the steps of: (a) defining or determining the proposed polymeric or copolymeric substance or material's molecular chemical composition through computation of individual polymer chain chemical composition; (b) computation properties of the molecular chemical composition when 3-dimensionally folded; (c) computing an assembly of the resulting 3-dimensionally folded molecular chemical composition into a polymeric of copolymeric cluster. The process preferably further includes the step (d) of computing or estimating the physical properties of polymeric or copolymeric cluster. In various preferred embodiments, step (a) involves estimating the molecular chemical composition as a function of conversion using kinetics rate theory; step (b) uses numerical methods based upon statistical thermodynamics such as Global Growth, In Situ Growth, Simple Phantom and Conditional Phantom Growth; step (c) uses molecular excluded volume constraints determined by vector geometry, and iterative use of numerical methods such as Global Growth, In Situ Growth, Simple Phantom, and Conditional Phantom Growth; and step (d) involves calculation of molecular energy expressions, or quantitative structure-property relationships such as thermal properties, optical properties, diffusion properties, and mechanical properties or a combination thereof. The instant process utilizes experimental information from chemical kinetics (reactivity ratios) and statistical thermodynamics (Boltzmann probabilities) to add physical as well as chemical constraints in the modeling of the polymer. As a result of the use of this invention, the modeling of polymers will rely less heavilv upon the intuition and expertise of the polymer modeler and more on the experimental information at hand. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate aspects of the invention and, together with the general description given above and the detailed description of the invention given below, serve to explain the principles of the invention. Fig. 1. is a computer generated picture of a three-dimensionally folded model of atactic polymethylmethacrylate; Fig. 2. is a computer generated picture of a three-dimensionally folded model of syndiotactic polymethylmethacrylate; Fig. 3. is a computer generated picture of a three-dimensionally folded model of isotactic polymethylmethacrylate; Fig. 4. is a schematic diagram illustrating the method for defining or determining the proposed polymeric or copolymeric substance or material's molecular chemical composition; Fig. 5. is a schematic diagram illustrating the Global Growth method; Fig. 6. is a schematic diagram illustrating the In Situ Growth method; Fig. 7. is a schematic diagram illustrating the Simple Phantom Growth method; Fig. 8. is a schematic diagram illustrating the Conditional Phantom Growth method; Fig. 9. is a schematic diagram illustrating the polymer assembly step using vector geometry; Fig. 10. is a schematic diagram illustrating the multi-chain growth method for simulating glassy polymer growth; Fig. 11. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) in accordance with Example 1, case a; Fig. 12. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) at 0% conversion in accordance with Example 1, case a; Fig. 13. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) at 20% conversion in accordance with Example 1, case a; Fig. 14. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) at 40% conversion in accordance with Example 1, case a; Fig. 15. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) at 60% conversion in accordance with Example 1, case a; Fig. 16. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) at 80% conversion in accordance with Example 1, case a; Fig. 17. is a computer generated picture of the resulting polymer wherein the starting materials were a 50:50 mixture of methyl acrylate (MA) and styrene (STY) at 99% conversion in accordance with Example 1, case a; Fig. 18. is a computer generated picture of the resulting polymer wherein the starting materials are a 23.3:76.7 mixture of methyl acrylate (MA) and styrene (STY) in accordance with Example 1, case b; Fig. 19. is a computer generated picture of the resulting polymer wherein the starting materials are a 23.3:76.7 mixture of methyl acrylate (MA) and styrene (STY) at 99% conversion in accordance with Example 1, case b; Fig. 20. is an example of (PHS) of the polymer clusters modeled in accordance with Example 2; Fig. 21. is a graph of projected free volume values versus group additivity free volumes in accordance with Example 2; Fig. 22. is a graph of projected free volume values versus the Log of experimentally obtained Oxygen Permeability in accordance with Example 2; Fig. 23. is a graph of projected free volume values versus the Log of experimentally obtained CO 2 permeability in accordance with Example 2; Fig. 24. is a graph of EGDMA weight fraction versus weight conversion, wherein the EGDMA monomer feed, the instantaneous, and the cumulative polymer compositions are shown as a function of total monomer weight conversion; Fig. 25. is a graph of MBAM weight fraction versus weight conversion, wherein the MBAM monomer feed and the instantaneous and cumulative polymer compositions are shown as a function of total weight conversion; Fig. 26. is a graph of weight fraction composition for a mixture of EGDMA and MBAM crosslinkers as a function of conversion in accordance with Example 3; and Fig. 27. is a schematic diagram showing the relationship between the different process steps embodied and broadly described by the present invention. THE INVENTION Applicants have succeeded in developing a process which combines a summary of molecular mechanics calculations on a variety of stable minima conformations, in the form of statistical weights or probabilities, and reaction kinetic expressions. Further, because the molecular structure is known at all points in the process, it is possible to compute the polymer's physical properties by employing quantitative structure property relationships or more advanced methods based on the spatial distribution and energetics of all the atoms in the polymer. Because many properties, including mechanical and diffusion, are a function of clusters of polymer molecules instead of isolated molecules, the present process provides methods whereby the clusters are assembled. Polymer Chemical Composition In a polymerization system containing two or more monomers, or monomer types (defined as different conformations of a monomer), the composition of the polymer being formed usually varies as a function of conversion. See Skeist, I.; J. of the American Chemical Society, 68 1781 (1946) which generalized the work of Alfrey, T. Jr., and C.C. Price; J. of Polymer Science, 2 101 (1947) and Walling and Briggs; J. of the American Chemical Society, 67 17741 (1945) and formulated a general equation to describe the change of composition as a function of conversion. Skeist's work is based on the analogy between the variation of polymer composition as a function of conversion and the Rayleigh equation which describes the composition of a binary distillation as a function of fraction distilled. Jean Guillot (Makromol. Chem., Macromol. Symp., 35/36, 269-289 (1990)) uses a different approach, applying reactivity ratios and empirically-fit kinetic equations to estimate copolymer bulk compositions. Once a method is obtained for estimating the composition of the polymer as a function of conversion, then the calculation of numerous physical properties, which depend on the polymer composition, can be readily performed. Step (a) of the instant inventive process enables determination of polymer compositions (monomer sequence distribution) of polymers containing from two to thirty different monomers or monomer types, using chemical reaction kinetics theory and experimentally measured monomer reactivities. Figure 4 is a schematic diagram for the steps of defining or determining the proposed polymeric of copolymeric substance or material's molecular chemical composition. The process illustrated in Figure 4 is, except for the operator-defined information and operator-controlled steps specified, a high level description of an algorithm which may be expressed in any suitable data processing language in accordance with programming techniques well known per se. The same applies to the process steps shown in Figures 5 to 10. The operator provides by way of any suitable peripheral input information relating to the attributes of the initial monomer mixture. As shown in the relevant schematic block of Figure 4, the monome mixture input information may comprise a number of monomer types (Nt), the 1-Th monomer type (M1), the concentration of the initial monomer (C1), the number of conversion values (Nc), the degree of polymerization (Dp) and an indication of batch or gradual addition of the various components. In any particular case, not all the attributes of the mixture need be specified, dependeing upon the sophistication of the program. The process shown in Figure 4 effectively ends with the saving of a complete monomer sequence for use in the process step B. It is possible if desired to display computed polymers on a graphics device and to stop the process if no polymer folding is indicated. 3-Dimensional Polymer Folding As stated above, small molecules have properties dependent mostly on the equilibrium structure of just a few conformations. A polymer has a hugh number of roughly equivalent "stable" structures. Experimentally it has been found that most polymer structures follow Gaussian statistics in their spatial distributions. An existing theory is able to explain such experimental findings, and mathematical expressions are available which reproduce the experimental results quite well. (See Flory, P.J., "Spatial Configuration of Macromolecular Chains", Nobel Lecture, December 11, 1974, in "Selected Works of Paul J. Flory, Mandelken, Mark, Suter, Yoon Eds. Standford University Press, V. 1. (5), 1985.) The instant invention utilizes such theory to estimate the shape, by three-dimensional folding, of large polymer molecules. The methods for growing polymers from the monomer sequence distribution are Global Growth, In Situ Growth and Simple and Conditional Phantom Growth. Each method uses techniques similar to the molecular statistical thermodynamic theories found in Flory, P.J., "Statistical Mechanics of Chain Molecules", Wiley-Interscience, New York. (1969). The determination or computation of a three-dimensional polymer folding is performed by any one of the algorithmic processes set out in Figures 5 to 8. The choice between these processes may be made at the point "Select STEP B" by the operator, who if he wishes more or less detailed information on the polymers, chooses whether the polymer folding is to be computed according to the global growth method, the in situ growth method, the simple phantom growth method or the conditional phantom growth method or the conditional phantom growth method. These are explained individually hereinafter. Global Growth Method In order for the polymer conformation to be representative of a Blotzmann distribution of energies at a temperature T, global minimization of a Molecular Mechanics force field is used to generate statistical weights for each of the possible conformations. For vinyl polymers without side chains there are 9 possible conformations at each new monomer addition tt tg+ tg- g+t g+g+ g+g- g-t g-g+ g-g- After global minimization of each of the nine structures, the energy is kept and probabilities are calculated from the minimized energies of each conformation (j=1,..,9) of the polymer including the i-th monomer: P ij = exp (-E ij /RT)/Z i where the E ij 's depend on the conformation of the previous i-1 monomers, and Z i = ΣP ij In theory this would provide the best answer to the determination of a Boltzmann distribution of polymer conformations at a given temperature T. In practice, however, global minimization is too slow for the size of polymers (i>10) of interest. The calculation of minimized energy grows at the rate of the square of the number of atoms in the chain (N**2). Therefore, approximations imply a reduction of the range and type of correlations between atoms along the polymer backbone. Others simplify the force field to speed up the calculations. Figure 5 is a schematic diagram illustrating the Global Growth method. In the global growth method shown in Figure 5, the operator needs to provide inputs, in any convenient signal form, specifying the input temperature, a random number and also an input specifying how large a chain (in terms of number of monomers) the properties of which the operator wishes to measure. The computation according to Figure 5 proceeds to the determination of a complete monomer chain. If the operator desires to have multi-chain information then he needs to select between step C1, the excluded volume method, and step C2, the multi-chain growth method, which are illustrated in Figures 9 and 10 respectively. In Situ Growth Method One method to mimic global optimization is by In Situ Growth. The In Situ method considers only the few monomers which are at the growing end of a chain. The method selects the number of monomers to use, usually 3, and a distance shell around these monomers to include in the surrounding atomic environment in the calculations. Then a new monomer is added in all its possible conformations (i.e. for a vinyl monomer, 9 sets of angles, for a chiral vinyl monomer, 18 sets of angles), each choice of monomer addition and set of angles describing each conformation is minimized with the force field, including only atoms in the chosen environment. The minimized energies are computed, and a probability vector is generated, with one value for each locally stable structure. These probabilities are temperature dependent. Probabilities are calculated from the minimized energies of each conformation (j=1,..,N) of the polymer including the i-th monomer following the equation P ij = exp (-E ij / RT)/Z i . A random number is generated from a uniform [0,1] distribution and the structure whose probability interval contains that random number is chosen. Then the method is repeated. The set of growing monomers slides down the chain by one monomer unit, and a new distance environment atom set is selected. All possible conformations are computed for adding a new monomer, and the probability vector regenerated from the energies. This approach can be used to predict solvation effects by including a representable, yet small, number of solvent molecules within the "sphere" of the growing polymer end. One way in which this method has been implemented is by the use of MacroModel's© substructure optimization, i.e., energy minimization is constrained to a sphere (usually a distance equal to 2 -10 Å) around the last monomer added to the chain. The rest of the chain remains fixed although a few atoms are still allowed to move slightly while being constrained by parabolic force constants. Figure 6 is a schematic diagram illustrating the In Situ Growth method. As in the case of the global growth method, for the in situ growth method the operator is required to select the inut temperature, a random number, the size of the growing end of the chain, i.e. distance for minimization and a number of movable monomers, and chooses how large a chain, in terms of number of monomers, of which the properties are to be measured. As in the Global Growth method, the computation of the process proceeds until the polymer chain is complete whereupon either step C1, the excluded Volume method, or step C2, the multi-chain growth method, is selected according to the wishes of the operator. Simple Phantom Growth Method The Simple Phantom Growth method reduces J.P. Flory's theory to a Simple Markov proces, wherein conformational probabilities depend only on the conformation of the previously added monomer. As such, nine probabilities of adding a monomer are calculated from energies of adding a single monomer to another. Long range effects are neglected. Implementation includes intradyad and interdyad probabilities. As an example consider the following polymer conformation sequence: (1) Polymer Conformation t g+ g+t t t g- g- g+t t g+ (2) Intradyad Conformations (tg+) (g+t) (tt) (g-g-) (g+t) (tg+) (3) Interdyad Conformations (g+g+) (tt) (tg-) (g-g+) (tt) Although this method is the fastest way to model the folding of polymer molecules, the shortness of range of correlations in neighboring polymer conformations result in highly coiled, and self-crossing (quite often one self-crossing every four monomers) chains. Consequently the energy distribution is highly non-Boltzmann. Figure 7 is a schematic diagram illustrating the Simple Phantom Growth method. Use of the Conditional Phantom Growth method greatly alleviates the non-Boltzmann distribution. Figure 7 illustrates the phantom polymer growth method. In this method the operator is required to set a temperature of interest and may choose how long a chain, in terms of number of monomers, of which the properties are to be measured. Once again, at the end of the polymer growth method, the operator needs to choose between the excluded volume method and the multi-chain growth method if the operator desires to examine multi-chain information. Conditional Phantom Growth Method In the Conditional Phantom Growth method, longer correlations are built by computing conformational probabilities from optimized trimers which include up to five bond correlations. Optimized "trimer" energies are used to calculated row vectors of conditional probabilities as follows: Once a conformation is selected for the current monomer, a van der Waals distance check is conducted. If large van der Waals repulsions are found then one monomer is backtracked and a different conformation is chosen (in the average the second most probable conformation will be chosen). If no conformation can be found to overcome van der Waals repulsion, the In Situ Growth method is used for optimization of the current monomer addition. Conditional Phantom Growth provides to the average probability weights which are good approximations to the average values obtained in the In Situ method. The folding of polymer molecules is quite efficient, hundreds of folded polymers can be calculated in a matter of minutes. The resulting energy distribution closely follows Boltzmann statistics, there is some long range conformation correlation and molecular energies are very often acceptable even though the initial monomer geometries used have only nominal values for t, g+, and g- angles. Quite often the molecular structures can be minimized without singularities when no self-crossing occurs. The backbone conformation preserves its general shape even after minimization and end-to-end distances vary by less than 1% after minimization. Figure 8 is a schematic diagram illustrating the Conditional Phantom Growth method. In the conditional phantom polymer growth method, the operator is required to select input temperature (T) and, if desires, how large a chain, in terms of monomers, of which the properties are to be measured. Once again, if the operator is required to select between steps C1 and C2, the excluded volume method and the multi-chain growth method respectively. In both the simple phantom polymer growth method and the conditional phantom polymer growth method there is at the end of the process a display of polymers on a graphics display and the display device will indicate whether a polymer is or can be assembled. If there is no assembly, the process may be stopped. Polymer Cluster Assembly An assembly of the resulting 3-dimensionally folded molecular composition into a proposed polymeric or copolymeric cluster is created as follows. From a statistical distribution of individual molecules, a polymer cluster reminiscent of the polymer in the bulk is computed. Then the model builder selects one of two methods; one method uses new excluded volume constraints derived from analytical vector geometry and the individual polymer structures of step (b). See figure (9), from "Molecular Silverware: General Solutions to Excluded Volume Constrained Problems", by Mario Blanco, Journal of Computational Chemistry, accepted for publication 7/3/90. The other uses the growth methods of step (b) iteratively to create a polymer assembly. Figure 10 is a schematic diagram illustrating the multi-chain growth method. The process of Figure 9 begins with the reading of the reading of the polymer structure from the relevant computer file created by the process performed as described with reference to any of Figures 5 to 8. the operator may now choose an arbitrary ordering scheme i.e. the order in which the chains are to be packed in an arbitrary volume. The process includes the display of the cluster on a graphics display so tha the operator can estimate how tightly or loosely the assembly is packed. If the result is unsatisfactory the operator may choose a different labelling scheme and the polymer cluster is re-computed according to the new labelling scheme. In the multi-chain polymer assembly method shown in Figure 10, the operator is required to choose the number of chains and the numbers of monomer per chain. The operator also selects the method of growing the chains, and for this purpose selects from the global method, the in situr method or the conditional phantom growth method. These are the same methods as described with reference to Figures 5, 6 and 8 previously. Whichever method is selected, the computation proceeds until all the possible chains are finished. One advantage of the disclosed methods growth of the polymeric clusters is that the data defining the created polymers can be used as the starting point for further examination and calculations. Moreover, many properties can be measured or displayed on a graphics device such as a computer screen. Estimation of the physical properties of the resulting clusters is achieved through the use of molecular energy expressions and/or quantitative structure property relationships, applied to the outcomes of steps (a), (b), and (c). Physical property estimation may also be achieved by any of the standard molecular modeling techniques, including but not limited to molecular mechanics, molecular dynamics, Monte Carlo methods, free energy perturbation methods, or the analytical vector geometry method described herein. Examples of properties computed are: Thermal Properties: Glass Transition Temperature Optical Properties: Refractive Index Diffusion Properties: Specific Free Volume CO 2 , O 2 Diffusion Coefficients Mechanical Properties: Elastic Moduli The following Examples serve to demonstrate aspects of the present inventive process. Example 1: Estimation of chemical molecular composition of a polymer reacted from a monomer mixture of methyl acrylate (MA) and styrene (STY). The chemical composition of a copolymer rarely remains constant both in its overall instantaneous monomer percent concentration and monomer sequence distribution, during the course of a polymerization reaction. The physical properties of the copolymer are highly dependent upon such chemical drifts. It is for this reason that the ability to estimate the molecular composition of a copolymer at different points during the reaction (percent conversion) is so important. As an example, consider the copolymerization of methyl acrylate with styrene. The present invention used published chemical kinetics experimental information (in the form of Q and e values) to estimate the composition of the copolymer as a function of conversion. Two cases are presented: Case a: The starting composition is a 50:50 mixture of MA and STY Case b: The starting composition is a 23:77 mixture of MA and STY Case a: The first line refers to the type of polymer chain to be generated, i.e. In Situ, linear, Phantom, Conditional Phantom. Polymer examples having a length of 50 monomers were requested. Figure 11 ia a computer generated picture of the instantaneous polymer composition as a function of monomer conversion. The conversion values are 0.0%, 25%, 50%, 75% and 100% from top to bottom respectively. Note the small drift in composition due to the differences in reactivities between the two monomers. This agrees with the experimental preparation of this polymer. Using the In Situ methods, three dimensional structures of these copolymers were generated from Case a. Figures 12-17 show the structural variations of these differing sequences copolymer chains at the 0%, 20%, 40%, 60% 80% and 99% monomer conversion levels. With a 50:50 mixture of starting material, a very different set of structures were achieved during the course of a batch reaction. This leads to a wide variation in polymer shapes and compositions. At the end of the reaction (close to 100% conversion), the copolymer that is being prepared is almost exclusively made up of methyl acrylate. Styrene has a larger preference for reacting with itself and it forms strings of connected styrene monomers even from the start of the reaction. At close to 100% conversion, most of the styrene has already been incorporated into the reacted polymer leaving only methyl acrylate to form polymer structures (Figure 17). Output Information ***Conversion-Composition (Mole Fraction Basis)*** # Monomer Name Q E MWT TG RI 1: MA 0.420 0.650 86.09 8.0 1.4800 2: STY 1.000 -0.800 104.14 100.0 1.5900 Conversion Monomer Mix Instantaneous Polymer Cumulative Polymer 0.000 Monomer 1 0.5000 0.3798 0.3798 0.000 Monomer 2 0.5000 0.6202 0.6202 Glass Trans Temp (Deg C) 63.0 63.0 Refractive Index 1.5530 1.5530 0.200 Monomer 1 0.5283 0.1944 0.3867 Monomer 2 0.4717 0.6056 0.6133 Glass Trans Temp (Deg C) 61.7 62.4 Refractive Index 1.5515 1.5523 0.400 Monomer 1 0.5697 0.4158 0.3955 Monomer 2 0.4303 0.5842 0.6045 Glass Trans Temp (Deg C) 59.7 61.6 Refractive index 1.5493 1.5514 0.600 Monomer 1 0.6384 0.4526 0.4077 Monomer 2 0.3616 0.5474 0.5923 Glass Trans Temp (Deg C) 56.2 60.4 Refractive Index 1.5453 1.5501 0.800 Monomer 1 0.7862 0.5502 0.4285 Monomer 2 0.2138 0.4498 0.5715 Glass Trans Temp (Deg C) 47.3 58.5 Refractive Index 1.5347 1.5479 0.990 Monomer 1 1.0000 1.0000 0.4949 Monomer 2 0.0000 0.0000 0.5051 Glass Trans Temp (Deg C) 8.0 52.3 Refractive Index 1.4800 1.5408 Case b: Only the initial monomer concentrations have been changed (methyl acrylate 0.233, styrene 0.767). Note that the drift in composition is drastically reduced when the initial monomer feed composition is changed from 50%:50% to 23.3%:76.7%. In fact, this composition is called the "azeotropic" composition. An azeotropic composition is that which balances the differences in reactivities of the two monomers by setting their initial concentrations such that at all times during the reaction the copolymer composition remains constant. Figure 18 shows the copolymer generated from example 1.b. In this case the polymer sequences and structures are the same throughout most of the reaction. Consequently, figure 18 is a polymer structure generated at all of the conversion levels 0%, 20%, 40%, 60% and 80%. The structure slightly changes at 99% and is shown in figure 19. The identical sequences and their similar three dimensional shapes create uniform polymer shapes and compositions during the course of the batch reaction. The present invention allows for the location of such azeotropic compositions over all or just part of the polymer reaction. This invention also shows the differing polymer shapes and consequently describes many of their properties such as size, volume, stiffness, and polymer structure. From these structures many other polymer design problems may be considered, such as packing, tensile strength, glass transition temperature, and other polymer properties which rely on the inherent three dimensional shape of polymers. Output Information ***Conversion-Composition (Mole Fraction Basis)*** # Monomer Name Q E MWT TG RI 1: MA 0.420 0.650 86.09 8.0 1.4800 2: STY 1.000 -0.800 104.14 100.0 1.5900 Conversion Monomer Mix Instantaneous Polymer Cumulative Polymer 0.000 Monomer 1 0.2330 0.2243 0.2243 Monomer 2 0.7670 0.7757 0.7757 Glass Trans Temp (Deg C) 77.8 77.8 Refractive Index 1.5688 1.5688 0.200 Monomer 1 0.2350 0.2257 0.2250 Monomer 2 0.7650 0.7743 0.7750 Glass Trans Temp (Deg C) 77.7 77.8 Refractive Index 1.5686 1.5687 0.400 Monomer 1 0.2378 0.2277 0.2258 Monomer 2 0.7622 0.7723 0.7742 Glass Trans Temp (Deg C) 77.5 77.7 Refractive Index 1.5684 1.5686 0.600 Monomer 1 0.2422 0.2307 0.2269 Monomer 2 0.7578 0.7693 0.7731 Glass Trans Temp (Deg C) 77.2 77.6 Refractive Index 1.5681 1.5685 0.800 Monomer 1 0.2510 0.2368 0.2285 Monomer 2 0.7490 0.7632 0.7715 Glass Trans Temp (Deg C) 76.6 77.4 Refractive Index 1.5675 1.5684 0.990 Monomer 1 0.3253 0.2843 0.2321 Monomer 2 0.6747 0.7157 0.7679 Glass Trans Temp (Deg C) 72.1 77.1 Refractive Index 1.5628 1.5680 Example 2: Estimation of the Gas Permeation Properties of Packaging Materials Containing Para-Hydroxy Stryene (PHS). A series of para-substituted polystyrenes were investigated to study the effect of chemical composition of free volume (Table I). Modeled Substituted Polystyrenes POLYMER X Density (gr/cc) Calculated Free Volume (cc/gr) Poly(p-chlorostyrene) PCS -CI 1.246 0.262 Poly(p-hydroxystyrene) PHS -OH 1.173 0.274 Poly(p-fluorostyrene) PFS -F 1.176 0.292 Poly(p-methoxystyrene) PMxS -OCH3 1.118 0.295 Poly(p-acetoxystyrene) PAS -OCOCH3 1.168 0.297 Poly(alpha-methylstyrene) PaMS -H (a) 1.065 0.299 Polystyrene PS -H 1.048 0.325 Poly(p-methylstyrene) PMS -CH3 1.009 0.346 Poly(p-t-butylstyrene) PtBS -C(CH3)3 0.947 0.396 For each of these polymer, the process of the instant invention was implemented. The degree of packing in the assembled cluster estimated in Step (d) also utilized the following expression: Vf (calculated) = 1/d -No Vc/Mc where "d" is the polymer's experimental density, "No" Avogadro's number, "Mc" and "Vc" the calculated mass and molar volume occupied by the assembly of polymer molecules. Figure 20 shows an example (PHS) of the polymer clusters modeled with the invention and used to calculate the quantities Mc and Vc in formula (3). Table I shows the calculated void volume values for the family of para-substituted polystyrenes. Figure 21 shows a comparison of the instant application's projected free volumes with those obtained using the experimentally based method commonly referred in the literature as "group additivity". Dirk W. van Krevelen, "Properties of Polymers, Their Estimation and Correlation with Chemical Structure", 2nd edition, Elsevier Science Publishers B.V., 1987. Applicants' predictions and the group additivity results are in good qualitative agreement. From the results one would conclude that among the para-substituted polystyrenes PHS has a low permeability because its void volume is among the lowest of the para-substituted styrenes while PtBS should have one of the highest permeabilities by virtue of its high free volume value. The predictions of the modeling of the instant invention were then compared against experimental data. The system of para-substituted polystyrenes has since been studied by Prof. Donald R. Paul, at the University of Texas at Austin. Puleo A.C., Muruganandam N., Paul, D.R. "Gas Sorption and Transport in Substituted Polystyrenes", Journal of Polymer Science Part B-Polymer Physics, 1989, V27, N11, pp. 2385-2406. Comparison was based upon a well known expression relating gas permeability and free volume values: P = P o exp (-V o /Vf) Taking logarithms on both sides of this expressions one should obtain straight lines when plotting Log P vs Log (1/Vf). The lines were fitted with a simple expression of the form: Log P (experimental) = Log P o -V o /Vf (calculated) where P o and V o are constants which depend only on the type of diffusing gas, e.g. oxygen or carbon dioxide. The experimental vs. projected values are shown in Tables II.A and II.B for oxygen and carbon dioxide respectively. The Experimental values are obtained from the Paul article. Table III contains the values of the constants in the permeability model. Experimental vs. Calculated (Eq.4) Oxygen Permeability Values PO 2 POLYMER X Exp. Pred. Poly(p-chlorostyrene) PCS -Cl 1.20 0.4357 Poly(p-hydrozystyrene) PHS -OH 0.12 0.7424 Poly(p-fluorostyrene) PFS -F 4.40 1.5214 Poly(p-methoxystyrene) PMxS -OCH3 2.60 1.7001 Poly(p-acetoxystyrene) PAS -OCOCH3 3.10 1.8284 Poly(alpha-methylstyrene) PaMS -H (a) 0.82 1.9646 Polystyrene PS -H 2.90 4.6111 Poly(p-methylstyrene) PMS -CH3 7.20 8.3645 Poly(p-t-butylstyrene) PtBS -C(CH3)3 35.50 26.7805 Experimental vs. Calculated (Eq.4) Carbon Dioxide Permeability Values PCO POLYMER X Exp. Pred. Poly(p-chlorostyrene) PCS -Cl 4.3 3.7303 Poly(p-hydroxystyrene) PHS -OH -- 5.6067 Poly(p-fluorostyrene) PFS -F 17.2 9.7025 Poly(p-methoxystyrene) PMxS -OCH3 18.9 10.5622 Poly(p-acetoxystyrene) PAS -OCOCH3 16.3 11.1666 Poly(alpha-methylstyrene) PaMS -H (a) 3.0 11.7968 Polystyrene PS -H 12.4 22.6472 Poly(p-methylstyrene) PMS -CH3 29.8 35.7041 Poly(p-t-butylstyrene) PtBS -C(CH3)3 140.1 86.9049 Gas Permeability Model Parameters GAS Po Vo Oxygen 02 11.340 3.188 Carbon Dioxide CO2 10.620 2.437 Figure 22 illustrates graphically the qualitative agreement between the model's predictions (Vf) and the experimental permeability results. Similar results are obtained for carbon dioxide permeability values (Figure 23). The fits indicate that the calculated Vf values are in good qualitative agreement with the experimentally measured gas permeabilities. Note in particular that the projections concerning the permeabilities of PHS and PtBS were confirmed by Prof. D. Paul's studies. PHS gives the lowest oxygen permeability values across the family of substituted styrenes while PtBS gives the highest value. Furthermore, it is clear that the low permeability of PHS is due greatly to a highly reduced polymer void volume, a quantity that is calculated using the polymer modeling methods of the instant invention. Example 3: Copolymerization Kinetics of Acrylic Acid (AA) with Sodium Acrylate (NaA) Using N,N'-Methylenebis(Acrylamide) (MBAM) and Ethylene Glycol Dimethacrylate (EGDMA) as Crosslinkers This Example demonstrates the use of the present invention to determine a suitable crosslinker for the copolymerization of Acrylic Acid with Sodium Acrylate. One wishes the crosslinker to be incorporated into the copolymer in a more or less uniform way throughout the reaction. Table II shows the estimated compositional drifts that should be observed with EGDMA as the crosslinker with a starting composition of AA/NaA/EGDMA : 22 / 77 / 1 by weight. Figure 24 is a graph of EGDMA weight fraction versus weight conversion. The EGDMA monomer feed and the instantaneous and cumulative polymer compositions are shown as a function of total monomer weight conversion. Note that the instantaneous copolymer composition at 90% conversion suffers an 8% depletion in EGDMA compared to the starting composition. Depletion of EGDMA continues and reaches 40% at 99% conversion. Crosslinker compositional drifts may be undesirable in such instances. The MBAM crosslinker is a better choice if one wishes the crosslinker to become incorporated into the polymer at a more uniform rate. Table III shows that for the same polymerization reaction the MBAM crosslinker drifts by less than 26% during the entire reaction. Based upon this chemical composition estimate, one would conclude that MBAM is a better choice of crosslinking agent. More detailed compositional information can also be estimated with the present invention. Table IV shows the chemical sequence distribution (Dyads, Triads, and monotonic sequences of length up to 20) for the AA/NaA/MBAM terpolymer as a function of conversion. Notice that the MBAM crosslinker (C) reacts five times more often with Sodium Acrylate (B) than with Acrylic Acid (A). For example, at 90% conversion the dyad distributions are AC+CA = 0.171% and BC+CB = 0.935%. This is not just the effect of the large concentration of Sodium Acrylate in the starting composition (77%) but also the result of the differences in the binary reactivity ratios. Table V shows these values. R1 is the ratio of reactivity of monomer 1 toward itself to the reactivity of monomer 1 toward monomer 2 (R1=k11/k12, R2=k22/k21). Thus, MBAM is 1.5 times (R2'/R2") more reactive toward Sodium Acrylate than toward Acrylic Acid. Finally, one may choose a balanced mixture of these two crosslinkers to obtain a more uniform incorporation of crosslinker throughout the reaction. Figure 26 shows the instantaneous polymer composition for a mixture consisting of 0.55% MBAM and 0.45% EGDMA. Note that because the individual crosslinking agents have opposite trends in their reactivities as a function of conversion, the selected mixture comes very close to the desired uniform incorporation of crosslinking agent throughout the entire conversion range. Ternary Copolymer Kinetics for AA/NaA/EGDMA Starting Composition: 22 / 77 / 1 : AA/NaA/EGDMA MONOMER NAME Q E Molecular Weight MONO 1 AA 1.1500 0.7700 72.06 MONO 2 NAA 0.7100 -0.1200 94.05 MONO 3 EGDMA 0.8800 0.2400 198.22 OUTPUT UNITS=WEIGHT FRACTIONS CONVERSION MONOMER MIX INSTANTANEOUS POLYMER CUMULATIVE POLYMER 0.000 MONOMER 1 0.2200 0.2835 0.2835 MONOMER 2 0.7700 0.7060 0.7060 MONOMER 3 0.0100 0.0106 0.0106 0.100 MONOMER 1 0.2133 0.2769 0.2803 MONOMER 2 0.7768 0.7126 0.7092 MONOMER 3 0.0099 0.0105 0.0106 0.200 MONOMER 1 0.2058 0.2694 0.2768 MONOMER 2 0.7843 0.7201 0.7127 MONOMER 3 0.0099 0.0105 0.0105 0.300 MONOMER 1 0.1973 0.2607 0.2729 MONOMER 2 0.7929 0.7288 0.7166 MONOMER 3 0.0098 0.0105 0.0105 0.400 MONOMER 1 0.1876 0.2507 0.2686 MONOMER 2 0.8028 0.7389 0.7209 MONOMER 3 0.0097 0.0104 0.0105 0.500 MONOMER 1 0.1761 0.2386 0.2639 MONOMER 2 0.8144 0.7511 0.7256 MONOMER 3 0.0095 0.0103 0.0105 0.600 MONOMER 1 0.1623 0.2237 0.2585 MONOMER 2 0.8284 0.7661 0.7311 MONOMER 3 0.0093 0.0102 0.0104 0.700 MONOMER 1 0.1449 0.2043 0.2522 MONOMER 2 0.8460 0.7857 0.7374 MONOMER 3 0.0091 0.0100 0.0104 0.800 MONOMER 1 0.1216 0.1770 0.2446 MONOMER 2 0.8698 0.8133 0.7451 MONOMER 3 0.0087 0.0098 0.0103 0.900 MONOMER 1 0.0862 0.1324 0.2349 MONOMER 2 0.9060 0.8585 0.7549 MONOMER 3 0.0078 0.0091 0.0102 0.990 MONOMER 1 0.0195 0.0337 0.2220 MONOMER 2 0.9758 0.9603 0.7679 MONOMER 3 0.0047 0.0060 0.0101 Ternary Copolymer Kinetics for AA/NaA/MBAM Starting Composition: 22 / 77 / 1 : AA/NaA/MBAM MONOMER NAME Q E Molecular Weight MONO 1 AA 1.1500 0.7700 72.05 MONO 2 NAA 0.7100 -0.1200 94.05 MONO 3 MBAM 0.7400 1.0000 154.17 OUTPUT UNITS=WEIGHT FRACTIONS CONVERSION MONOMER MIX INSTANTANEOUS POLYMER CUMULATIVE POLYMER 0.000 MONOMER 1 0.2200 0.2836 0.2836 MONOMER 2 0.7700 0.7082 0.7082 MONOMER 3 0.0100 0.0082 0.0082 0.100 MONOMER 1 0.2133 0.2770 0.2804 MONOMER 2 0.7765 0.7147 0.7113 MONOMER 3 0.0102 0.0084 0.0083 0.200 MONOMER 1 0.2058 0.2694 0.2768 MONOMER 2 0.7838 0.7220 0.7148 MONOMER 3 0.0104 0.0086 0.0084 0.300 MONOMER 1 0.1973 0.2607 0.2730 MONOMER 2 0.7921 0.7303 0.7185 MONOMER 3 0.0106 0.0089 0.0085 0.400 MONOMER 1 0.1875 0.2506 0.2687 MONOMER 2 0.8016 0.7401 0.7227 MONOMER 3 0.0109 0.0093 0.0087 0.500 MONOMER 1 0.1761 0.2385 0.2639 MONOMER 2 0.8127 0.7518 0.7273 MONOMER 3 0.0112 0.0097 0.0088 0.600 MONOMER 1 0.1623 0.2235 0.2585 MONOMER 2 0.8262 0.7663 0.7325 MONOMER 3 0.0115 0.0101 0.0090 0.700 MONOMER 1 0.1449 0.2041 0.2522 MONOMER 2 0.8432 0.7852 0.7386 MONOMER 3 0.0119 0.0107 0.0092 0.800 MONOMER 1 0.1217 0.1769 0.2446 MONOMER 2 0.8660 0.8117 0.7460 MONOMER 3 0.0122 0.0115 0.0094 0.900 MONOMER 1 0.0865 0.1325 0.2348 MONOMER 2 0.9009 0.8551 0.7555 MONOMER 3 0.0125 0.0125 0.0097 0.990 MONOMER 1 0.0199 0.0343 0.2220 MONOMER 2 0.9695 0.9537 0.7680 MONOMER 3 0.0106 0.0120 0.0100 BINARY REACTIVITY RATIOS BINARY REACTION REACTIVITY RATIOS 1 ACRYLIC ACID R1 = 0.8162 2 SODIUM ACRYLATE R2 = 0.5549 1 ACRYLIC ACID R1'= 1.8552 2 MBAM R2'= 0.5113 1 SODIUM ACRYLATE R1"= 0.8388 2 MBAM R2"= 0.3401 Additional advantages and modifications will readily occur to those skilled in the art. The invention in its broader aspects is, therefore, not limited to the specific details, representative apparatus and illustrative examples shown and described. Accordingly, departures may be made from such details.

Claims: A process performed with the aid of a programmed computer for the estimation of physical properties of a proposed polymeric or copolymeric substance comprising the steps of: inputting information representating attributes of monomers; computing the molecular chemical composition of the proposed polymeric or copolymeric substance through estimation of individual polymer chain chemical composition; computing properties of the molecular chemical composition when 3-dimensionally folded; computing a model of the resulting 3-dimensionally folded molecular chemical composition as a proposed polymeric or copolymeric cluster; and computting from said model selected physical properties of the resulting polymeric or copolymeric cluster. A process according to claim 1 wherein an individual polymer chain chemical composition is computed as a function of conversion, and reaction time using chemical kinetics rate expressions. A process according to claim 1 or claim 2 wherein the properties of the molecular chemical composition when 3-dimensionally folded are computed by use of a numerical method of statistical thermodynamics. A process according to claim 3 wherein the numerical method is selected by an operator and comprises any one of a global growth method, an in situ growth method, a simple phantom growth method and a conditional phantom growth method. A process according to claim 4 wherein the proposed substance has more than five monomer units and the said numerical method comprises any one of the in situ method, the simple phantom growth method and the conditional phantom growth method. A process according to any of claims 3 to 5 wherein the effect of a solvent, or any other small molecule, on the molecular chain chemical composition and 3-dimensional folding is computed through in situ optimization. A process according to any of claims 3 to 6 wherein the numerical method comprises a conditional phantom growth method including a van der Walls distance check. A process according to any foregoing claim wherein the computation of the model comprises the use of molecular excluded volume constraints. A process according to any of claims 1 to 7 wherein the computation of the model comprises the iterative application of a numerical method of statistical thermodynamics. A process according to claim 9 wherein the numerical method is operator-selected and optionally comprises any one of global growth, in situ growth, simple phantom growth and conditional phantom growth. A process according to any foregoing claim, wherein the computation of physical properties of the resulting polymeric or copolymeric cluster comprises calculating molecular energy expressions, or quantitative property relationships such as thermal properties, optical properties, diffusion properties, and mechanical properties or a combination thereof. A process according to any foregoing claim, futher comprising developing a format for the synthesis of the proposed substance. A process according to any foregoing claim wherein said polymeric or copolymeric substance or material is a plastic, packaging material, optical disk material, or barrier membrane, adhesive, viscosity improver, dispersant, electronic chemical, coating, or synthetic biopolymer. A process according to claim 13 wherein said plastic is a polymer blend compatibilizer, high temperature plastic, or thermoplastic elastomeric, amorphous, crystalline, or liquid crystalline polymer or polymer blend. A process according to claim 14 wherein said barrier membrane is a bio-separator material. A process according to any foregoing claim and including generating a visual display of the said cluster.

Inventors: BLANCO MARIO, PIERCE THOMAS HENRY, BLANCO, MARIO, PIERCE, THOMAS HENRY
Applicants: ROHM & HAAS, ROHM AND HAAS COMPANY
Application year: 1992
Main classification: G06F17
IPC codes: G06F17, G06F19, C07K1