Prediction of elongation at break for linear polymers

• The ability to predict material properties previous synthesis leads to tremendous savings intime and cost.
• Prediction of elongation at break (target property) for a group of linear polymers is presented.
• New descriptors are proposed to better represent structural features, including experimental parameters.
• The developed MLP neural network model showed good prediction metrics and was internally and externally validated.

In this paper we present results on prediction of elongation at break (target property) for a group of 77 amorphous polymers of high molecular weight. Novel descriptors are proposed in order to better represent structural features related to the target property. These proposed descriptors along with the classic ones, were calculated for the set of polymers. The final descriptors of the predictive model were obtained by using a combination of variable selection method and domain knowledge. The model consisted of three descriptors: Cross-head Speed (CHS), Number Average Molecular Weight/Main Chain Surface Area ratio (Mn/SAMC), and Normalized Main Chain Mass (nMMC). By means of a multi-layer perceptron (MLP) neural network a good prediction model (R2 = 0.88 and MAE = 1.89) was achieved, which was internally and externally validated. The model shows the advantages of using well-known parameters in the field of polymers and of capturing the structural characteristics of the main and side chains. Thus, more intelligent tools are developed for the design of new materials with a specific application profile.