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

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 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 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 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.