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 Khiêm-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 longstanding gap problem.

A Machine Learning Study of Polymer-Solvent Interactions

Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction(χ),Hildebrand solubility(Δδ)and the relative energy difference(RED)determined from Hansen solubility in experimental,theoretical and simulation studies.Here we performed a machine learning study based on a comprehensive and representative dataset covering the interaction pairs from 81polymers and 1221 solvents.The regression models provide the coefficients of determination in the range of 0.86-0.94 and the classification models deliver the area under the receiver operating characteristic curve(AUCs)better than 0.93.These models were integrated into a newly developed software polySML-PSI.Important features including Log P,molar volume and dipole are identified,and their non-linear,nonmonotonic contributions to polymer-solvent interactions are presented.The widely known“like-dissolve-like”rule and two broadly used empirical equations to estimateχas a function of temperature or Hansen solubility are also evaluated,and the polymer-specified constants are presented.This study provides a quantitative reference and a tool to understand and utilize the concept of polymer-solvent interactions.