Designing Polymer Blends Using Neural Networks, Genetic Algorithms, and Markov Chains

In this paper we present a new technique to simulate polymer blends that overcomes the shortcomings in polymer system modeling. This method has an inherent advantage in that the vast existing information on polymer systems forms a critical part in the design process. The stages in the design begin with selecting potential candidates for blending using Neural Networks. Generally the parent polymers of the blend need to have certain properties and if the blend is miscible then it will reflect the properties of the parents. Once this step is finished the entire problem is encoded into a genetic algorithm using various models as fitness functions. We select the lattice fluid model of Sanchez and Lacombe1 , which allows for a compressible lattice. After reaching a steady-state with the genetic algorithm we transform the now stochastic problem that satisfies detailed balance and the condition of ergodicity to a Markov Chain of states. This is done by first creating a transition matrix, and ...

• Type: Journal
• Year: 2004
• Where: APIN
• Authors: N. K. Roy, Walter D. Potter, D. P. Landau
• Keywords: APIN 2004, Genetic Algorithm, Polymers, Vast Existing Information