Computer-Aided Design of High Performance Polymers

Abstract

The purpose of this work is to develop a systematic procedure for the design of polymer molecules so as to satisfy a set of specified desired proper ties. An optimization-based approach is developed to synthesize the polymer from a set of chemical groups. The synthesis task is formulated as a mixed- integer nonlinear program which seeks to generate candidate polymers that realize the desired properties along with the constraints on structural feasibil ity and end-use considerations. Group-contribution methods are employed to correlate the performance criteria with the chemical structure of the monomer molecules. New structural feasibility constraints are introduced to ensure the soundness of the designed polymer. An illustrative example is given to demon strate the applicability of the proposed procedure.

Analytical Rheology of Polymer Melts: State of the Art

“…It should be pointed out that, while there is considerable overlap, the goal of analytical rheology is somewhat different from that of in silico design of polymeric materials [7][8][9][10][11]. Analytical rheology is a diagnostic tool, which is useful to determine the microstructure of a particular sample.…”

Design of formulated products: Experimental component

“…Computer-aided methods have been developed for molecular design, 14 solvent design, [15][16][17][18] mixture design, 19 polymer design, [20][21][22][23] and refrigerant design. 24,25 The algorithms employed can be classified under the following types: ''generate and test'' algorithm, 15,17,24,26 genetic algorithm, 22 mathematical programing, 16,21 component-less design techniques, 27,28 combinatorial optimization, 29 and hybrid methods. 14 …”

Design of improved permeation enhancers for transdermal drug delivery

“…The combinatorial complexity associated with CAMD makes exhaustive or heuristic search techniques (rational design) less effective than GAs (random design) for molecular design problems. Several algorithms have been proposed for handling searches in constrained molecular design domains, including deterministic methods such as expert systems,29, 30 heuristic enumeration,31 machine learning, and mathematical programming‐based strategies32–34 as well as stochastic approaches such as GAs23, 26 and simulated annealing. Although each method has advantages, the models suffer from drawbacks attributable to the combinatorial complexity of the search space and the problematic incorporation of nonlinear structure–property knowledge.…”