Polymers computational prediction

An area of great interest in the polymer chemistry field is structure-activity relationships. In the simplest form, these can be qualitative descriptions, such as the observation that branched polymers are more biodegradable than straight-chain polymers. Computational simulations are more often directed toward the quantitative prediction of properties, such as the tensile strength of the bulk material. 

Dineiro, Y. Menendez, M. I. Lopez, M. C. B. Jesus, M. Lobo.C. Ordieres, A. J. M. Blanco, P. T., Computational predictions and experimental affinity distributions for a homovanillic acid molecularly imprinted polymer, Biosens. Bioelectron. 2006, 22, 364—371... 

Azimi,A., Javanbakht, M. (2014). Computational prediction and experimental selectivity coefficients for hydroxyzine and cetirizine molecularly imprinted polymer based potentiometric sensors, AnaL Qiinh 812,184-190. 

Aerts, J. Prediction of intrinsic viscosities of dendritic, hyperbranched and branched polymers. Computational and Theoretical Polymer Science, 8,49-54 (1998). 

Hence, the adduced in the present chapter techniques can be considered as the physical base for computer prediction and simulation of film polymer samples at quasistatic tension. It is important to emphasize that in theoretical calculation not a single experimentally determined parameter is not used, but the initial characteristics (8, 5 or IT, E, are taken from literary sources [1],... 

We have briefly reviewed a few applications of our CG model for MD simulations of amphiphilic polymers to predict their self-assembly into supramolecular structures with surprising detail. We speculate that, in the near future, the assembly into complex supramolecular structures will be studied as easily as chemical reactions between small molecules have been modeled successfully in the past few decades by electronic stmcture methods. However, several potential issues may arise first and foremost, the accuracy of the CG models used will be put to the test, because higher computing power will allow the study of larger and more complex systems. 

Basic Definitions and Computational Prediction of Fundamental Optical Properties of Polymers... 

Case, F.H. Applications of modeUng in polymer-property prediction,/ Computer-Aided Mater. Design, 3,369-378 (1996). 

The complexity of polymeric systems make tire development of an analytical model to predict tlieir stmctural and dynamical properties difficult. Therefore, numerical computer simulations of polymers are widely used to bridge tire gap between tire tlieoretical concepts and the experimental results. Computer simulations can also help tire prediction of material properties and provide detailed insights into tire behaviour of polymer systems. A simulation is based on two elements a more or less detailed model of tire polymer and a related force field which allows tire calculation of tire energy and tire motion of tire system using molecular mechanisms, molecular dynamics, or Monte Carlo teclmiques 1631. 

Some researchers use molecule computations to estimate the band gap from the HOMO-LUMO energy separation. This energy separation becomes smaller as the molecule grows larger. Thus, it is possible to perform quantum mechanical calculations on several molecules of increasing size and then extrapolate the energy gap to predict a band gap for the inhnite system. This can be useful for polymers, which are often not crystalline. One-dimensional band structures are... 

Many of the mesoscale techniques have grown out of the polymer SCF mean field computation of microphase diagrams. Mesoscale calculations are able to predict microscopic features such as the formation of capsules, rods, droplets, mazes, cells, coils, shells, rod clusters, and droplet clusters. With enough work, an entire phase diagram can be mapped out. In order to predict these features, the simulation must incorporate shape, dynamics, shear, and interactions between beads. 

The rotational isomeric state (RIS) model assumes that conformational angles can take only certain values. It can be used to generate trial conformations, for which energies can be computed using molecular mechanics. This assumption is physically reasonable while allowing statistical averages to be computed easily. This model is used to derive simple analytic equations that predict polymer properties based on a few values, such as the preferred angle... 

Once a polymer geometry has been described, it can be used to predict density, porosity, and so forth. Geometry alone is often of only minor interest. The purpose of computational modeling is often to determine whether properties of the material justify a synthesis elfort. Some of the properties that can be predicted are discussed in the following sections. 

Polymers can be crystalline, but may not be easy to crystallize. Computational studies can be used to predict whether a polymer is likely to crystallize readily. One reason polymers fail to crystallize is that there may be many conformers with similar energies and thus little thermodynamic driving force toward an ordered conformation. Calculations of possible conformations of a short oligomer can be used to determine the difference in energy between the most stable conformer and other low-energy conformers. 

Polymers will be elastic at temperatures that are above the glass-transition temperature and below the liquiflcation temperature. Elasticity is generally improved by the light cross linking of chains. This increases the liquiflcation temperature. It also keeps the material from being permanently deformed when stretched, which is due to chains sliding past one another. Computational techniques can be used to predict the glass-transition and liquiflcation temperatures as described below. 

CHEOPS (we tested Version 3.0.1) is a program for predicting polymer properties. It consists of two programs The analysis program allows the user to draw the repeat unit structure and will then compute a whole list of properties the synthesis program allows the user to specify a class of polymers and desired properties and will then try the various permutations of the functional groups to find ones that fit the requirements. On a Pentium Pro 200 system, the analysis computations were essentially instantaneous and the synthesis computations could take up to a few minutes. There was no automated way to transfer information between the two programs. 

CHEOPS is based on the method of atomic constants, which uses atom contributions and an anharmonic oscillator model. Unlike other similar programs, this allows the prediction of polymer network and copolymer properties. A list of 39 properties could be computed. These include permeability, solubility, thermodynamic, microscopic, physical and optical properties. It also predicts the temperature dependence of some of the properties. The program supports common organic functionality as well as halides. As, B, P, Pb, S, Si, and Sn. Files can be saved with individual structures or a database of structures. 

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