NEURAL NETWORKS AND GRAPH-THEORY AS COMPUTATIONAL TOOLS FOR PREDICTING POLYMER PROPERTIES

A new computational methodology is presented for making rapid and accu rate predictions of chemical, physical and mechanical properties of po lymers based on their molecular structure. The method uses a set of to pological indices derived from graph theory to numerically describe th e structure of a monomeric repeating unit for a given polymer (structu ral descriptors) and relates these indices to a set of polymer propert ies by utilizing an artificial neural network. The neural network is a ble to efficiently formulate all of the correlations (i. e., between s tructural descriptor-property, property-property, structural descripto r-structural descriptor: both linear and nonlinear dependencies) neces sary to make accurate predictions. Results have been obtained for up t o 9 properties of 357 different polymers with an average prediction er ror of < 3% and a maximum error of 12%, demonstrating superiority over other quantitative structure/property relationships for polymers.