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

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Polyhydroxyalkanoate-based polymers-being eco-friendly, bio-synthesizable, 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… Click to show full abstract

Polyhydroxyalkanoate-based polymers-being eco-friendly, bio-synthesizable, 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 Tg 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 Tg values, polymer molecular weights and polydispersity index for PHA-based homo- and co-polymers 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 provides uncertainties underlying the predictions. The model is further combined with an evolutionary algorithm-based search strategy to efficiently identify multi-component polymer compositions with a pre-specified Tg. While the present contribution is focused specifically on Tg, the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

Keywords: machine learning; polyhydroxyalkanoate; polymer; property; glass transition

Journal Title: Journal of chemical information and modeling
Year Published: 2019

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