Modelling Structure-Property Relationship for Copolymers by Structured Representation of Repeating Units.

We report here a recent study on the prediction by recursive neural network of the glass transition temperature of (meth)acrylic copolymers, for which appropriate structured representations are proposed. It is shown that the flexibility of such description allows for simultaneously treating different classes of compounds as well as accounting for different average properties such as tacticity and molar composition. [ABSTRACT FROM AUTHOR]