Neural networks and graph theory as computational tools for predicting polymer properties

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Abstract
A new computational methodology is presented for making rapid and accurate predictions of chemical, physical and mechanical properties of polymers based on their molecular structure. The method uses a set of topological indices derived from graph theory to numerically describe the structure of a monomeric repeating unit for a given polymer (structural descriptors) and relates these indices to a set of polymer properties by utilizing an artificial neural network. The neural network is able to efficiently formulate all of the correlations (i.e., between structural descriptor‐property, property‐property, structural descriptor‐structural descriptor: both linear and nonlinear dependencies) necessary to make accurate predictions. Results have been obtained for up to 9 properties of 357 different polymers with an average prediction error of < 3% and a maximum error of 12%, demonstrating superiority over other quantitative structure/property relationships for polymers.