Designing Novel Polymers with Targeted Properties Using the Signature Molecular Descriptor

A method for solving the inverse quantitative structure−property relationship (QSPR) problem is presented
which facilitates the design of novel polymers with targeted properties. Here, we demonstrate the efficacy
of the approach using the targeted design of polymers exhibiting a desired glass transition temperature, heat
capacity, and density. We present novel QSPRs based on the signature molecular descriptor capable of
predicting glass transition temperature, heat capacity, density, molar volume, and cohesive energies of linear
homopolymers with cross-validation squared correlation coefficients ranging between 0.81 and 0.95. Using
these QSPRs, we show how the inverse problem can be solved to design poly(N-methyl hexamethylene
sebacamide) despite the fact that the polymer was used not used in the training of this model.