Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers

Abstract

Polyhydroxyalkanoate-based polymersbeing ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable propertiesare currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. The burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature T g as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure–property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured T g values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified T g. While the present contribution is focused specifically on T g, the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

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Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model

“…There are several advantages of the feature representation adopted in this work. The use of polymer repeat units is more reasonable than that of monomers as the former is a building block of the corresponding polymers, though the use of polymer monomers has been widely adopted in polymer informatics [ 39 , 87 , 88 ]. This is probably due to the requirements of cheminformatics packages on the SMILES strings that can be processed.…”

Machine learning discovery of high-temperature polymers

“…The effect of using different representations on T g estimation has been demonstrated through systematic representation evaluation 72 or separate model development. [37][38][39]42,43,[50][51][52][53][54][55][56][57] In addition, the development of new representations remains critical for the development of high-performance ML models. To carry out a thorough study considering different types of representations, we explore three types of feature representation based on the SMILES notation of each polymer: molecular descriptors, Morgan fingerprints, and images, as presented in Figure 2.…”