The glass transition temperature (T g) is a fundamental property of polymers that strongly
influences both mechanical and flow characteristics of the material. In many important …

Abstract Machine learning (ML) offers promising tools to develop surrogate models for
polymers' structure–property relations. Surrogate models can be built upon existing polymer …

Predicting the properties of complex polymeric materials based on monomer chemistry
requires modeling physical interactions that bridge molecular, interchain, microstructure …

Polyurethanes are a broad class of material that finds application in coatings, foams, and
solid elastomers. The urethane chemistry allows a diversity of monomers to be used, and …

Bridging the gap between the computation of mechanical properties and the chemical
structure of elastomers is a long-standing challenge. To fill the gap, we create a raw dataset …

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 …

As an important thermophysical property, polymers' glass transition temperature, Tg, could
sometimes be difficult to determine experimentally. Modeling methods, particularly data …

The glass transition temperature of polymers is a key parameter in meeting the application
requirements for energy absorption. Previous studies have provided some data from slow …

We used fully connected artificial neural networks (ANN) to localize and quantify, based on
the monomer structure of several polymers, the specific features responsible for their …

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 …