Hierarchical Machine Learning Model for Mechanical Property Predictions of Polyurethane Elastomers From Small Datasets

Abstract

Polyurethanes are a broad class of material that finds application in coatings, foams, and solid elastomers. The urethane chemistry allows a diversity of monomers to be used, and prediction of mechanical properties, which are determined by complex interplay between monomer chemistry and chain architecture, is an unresolved challenge. Urethanes are based on aromatic or cyclic isocyanates and linear or branched polyols, and polymerization results in linear chains for bifunctional monomers or branched chains for multifunctional monomers. Strong intermolecular interactions between aromatic groups result in the formation of hard-segment domains that generate physical crosslinks between disorganized rubbery domains and anchor the material microstructure, contributing to resistance to deformation. Here, a general hierarchical machine learning (HML) model for predicting the stress-at-break, strain-at-break, and Tan δ for thermoplastic and thermoset polyurethanes is presented. The algorithm was trained on a library of 18 polymers with different diisocyanates, bifunctional or trifunctional polyols, and NCO:OH index. HML reduces data requirements through robust embedding of domain knowledge and surrogate data in a middle layer that bridges input variables (composition) and output responses (mechanical properties). In this work, the middle layer included information on overall polymer composition, predictions of chain architecture derived from Monte Carlo simulations of polymerization, information on interchain interactions from empirically derived molecular potentials and shifts in infrared (IR) spectroscopy absorbances. The HML predictions are shown to be more accurate than those from a random forest model directly relating composition and properties, suggesting that embedding domain knowledge provides significant advantages in predicting the properties of complex material systems based on small datasets.

Advancing cement-based materials design through data science approaches

“…Similarly, Menon et. al [23] predicted the Young's modulus of polyurethanes through relating the molecular composition to a middle layer of physicochemical properties utilizing stochastic simulation and molecular modeling.…”

Data-Efficient Graph Grammar Learning for Molecular Generation

“…Particularly in the context of polymers, large amounts of training data are not available, and therefore DL models use manually constructed or generated data (St. John et al, 2019;Jin et al, 2020;Ma & Luo, 2020). Real data sets, as used in one of the state-of-the-art papers on polyurethane property prediction, have as little as 20 samples (Menon et al, 2019). In such scenarios, designing a pure DL-based model is challenging.…”

Small data materials design with machine learning: When the average model knows best

“…28 In contrast, Menon et al presented a Hierarchical Machine Learning (HML) model, which provides useful predictions on the mechanical properties of elastomers or predict novel dispersants using only a handful of experiments. 46,47 Although these examples show that, even in the context of small datasets, the use of AI and ML can be successful for materials research, the quality of the obtained models is often defined in an ad hoc fashion and their dependence on the used dataset is not discussed.…”