Recursive neural networks prediction of glass transition temperature from monomer structure. An application to acrylic and methacrylic polymers

We propose a new method based on a Recursive Neural Network (RecNN) for predicting polymer properties from their structured molecular representations. RecNN allows for a completely novel approach to QSPR analysis by direct adaptive processing of molecular graphs. This model joins the representational power of structured domains with Neural Network ability to capture underlying complex relationships in the data by a process of training from examples. To this aim, a structured representation was designed for the modelling of polymer structures. The adopted representation can account also for average macromolecule characteristics, such as degree of polymerization, stereoregularity, comonomer distribution. To begin with, this model was applied to the prediction of the glass transition temperature of ( meth) acrylic polymers with different degree of main chain tacticity. The results so far obtained indicate that the proposed representation of polymer structure can convey information on both the repeating unit structure and average polymer features. The ability of the proposed RecNN method of treating this structured representation makes this method more general and flexible with respect to standard literature methods. Moreover, the same model can handle at the same time the Tg of polymer samples present in only one tacticity form together with that of polymer with different stereoregularity