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 …

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 …

Establishing the structure–property relationship by machine learning (ML) models is
extremely valuable for accelerating the molecular design of polymers. However, existing ML …

This article investigates the utility of machine learning (ML) methods for predicting and
analyzing the diverse physical characteristics of polymers. Leveraging a rich dataset of …

Polymeric materials are finding increasing application in commercial optical communication
systems. Taking advantage of techniques from the field of artificial intelligence, the goal of …

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …

Abstract Glass Transition Temperature is one of the most studied fields in material science
and measurement of Glass Transition Temperatures for the ever-expanding list of polymers …

Our goal is to establish the applicability of artificial neural networks to the prediction of
physical and mechanical polymer properties from their monomer structures alone. We …

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 …