Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning

Bridging the gap between the computation of mechanical properties and the chemical structure of elastomers is a long-standing challenge. To fill the gap, we create a raw dataset and build predictive models for Young's modulus, tensile strength, and elongation at break of polyurethane elastomers (PUEs). We then construct a benchmark dataset with 50.4% samples remained from the raw dataset which suffers from the intrinsic diversity problem, through a newly proposed recursive data elimination protocol. The coefficients of determination (R(2)s) from predictions are improved from 0.73-0.78 to 0.85-0.91 based on the raw and the benchmark datasets. The fitting of stress-strain curves using the machine learning model shows a slightly better performance than that for one of the well-performed constitutive models (e.g., the Khiem-Itskov model). It confirmed that the black-box machine learning models are feasible to bridge the gap between the mechanical properties of PUEs and multiple factors for their chemical structures, composition, processing, and measurement settings. While accurate prediction for these curves is still a challenge. We release the raw dataset and the most representative benchmark dataset so far to call for more attention to tackle the long-standing gap problem.