Predicting Young’s Modulus of Linear Polyurethane and Polyurethane–Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning

Predicting
the properties of complex polymeric materials based
on monomer chemistry requires modeling physical interactions that
bridge molecular, interchain, microstructure, and bulk length scales.
For polyurethanes, a polymer class with global commercial and industrial
significance, these multiscale challenges are intrinsic due to the
thermodynamic incompatibility of the urethane and polyol-rich domains,
resulting in heterogeneities from molecular to microstructural length
scales. Machine learning can model patterns in data to establish a
relationship between the monomer chemistry and bulk material properties,
but this is made difficult by small data sets and a diverse set of
monomers. Using a data set of 63 industrially relevant and complex
elastomers, we demonstrate that accurate machine learning predictions
are possible when monomer chemistry is used to estimate interactions
at interchain length scales. Here, these features were used to accurately
(r2 = 0.91) predict the Young’s
modulus of polyurethane and polyurethane–urea elastomers. Furthermore,
by a query of the trained model for compositions that yield a target
modulus within the range of accessible values, the capabilities of
using this methodology as a design tool are demonstrated. The presented
methodology could become increasingly useful in building models for
materials with small data sets and may guide the interpretation of
the underlying physicochemical forces.