Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …

Diminishing fossil fuel-based resources and ever-growing environmental concerns related
to plastic pollution demand for the development of sustainable and biodegradable polymeric …

Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and
economically viable and possessing a broad range of tunable properties—are currently …

In this work convolutional-fully connected neural networks were designed and trained to
predict the glass transition temperature of polymers based only on their chemical structure …

Technological advances and the need for new polymers necessitate continuous research in
the design and identification of polymers with specific physical and chemical properties …

Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and
current technologies are impossible to sustain without plastics. Bio-synthesized and …

To formulate a machine learning (ML) model to establish the polymer's structure-property
correlation for glass transition temperature T g, we collect a diverse set of nearly 13,000 real …

Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons
were derived using a back-propagation neural network and a modified Fuzzy ARTMAP …

Artificial neural networks (ANNs) have been successfully used in the past to predict different
properties of polymers based on their chemical structure and to localize and quantify the …

Due to outstanding performance in cheminformatics, machine learning algorithms have
been increasingly used to mine molecular properties and biomedical big data. The …