Accurately predicting new polymers' properties with machine learning models apriori to
synthesis has potential to significantly accelerate new polymers' discovery and …

Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …

Over the past decade, there has been a resurgence in the importance of data-driven
techniques in materials science and engineering. The utilization of state-of-the art …

Polymer Genome is a web-based machine-learning capability to perform near-
instantaneous predictions of a variety of polymer properties. The prediction models are …

Glass transition temperature, Tg, is an important thermophysical property of
polyacrylamides, which can be difficult to determine experimentally and resource-intensive …

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

Semiconducting donor–acceptor (D–A) polymers have attracted considerable attention
toward the application of organic electronic and optoelectronic devices. However, a rational …

Establishing the structure-property relationship is extremely valuable for the molecular
design of copolymers. However, machine learning (ML) models can incorporate both …

Machine learning (ML) and artificial intelligence (AI) have remarkable abilities to classify,
recognize, and characterize complex patterns and trends in large data sets. Here, we adopt …

Quantitative structure− property relationships (QSPR) are developed to correlate glass
transition temperatures and chemical structure. Both monomer and repeat unit structures are …