We propose a chemical language processing model to predict polymers' glass transition
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …

The use of machine learning in computational molecular design has great potential to
accelerate the discovery of innovative materials. However, its practical benefits still remain …

Predicting the glass-transition temperatures (T g) of glass-forming polymers is of critical
importance as it governs the thermophysical properties of polymeric materials. The …

Predicting the glass transition temperature (T g) is of critical importance as it governs the
thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive …

We present machine learning models for the prediction of thermal and mechanical
properties of polymers based on the graph convolutional network (GCN). GCN-based …

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 …

Developing amorphous polymers with desirable thermal conductivity has significant
implications, as they are ubiquitous in applications where thermal transport is critical …

A necessary transformation for a sustainable economy is the transition from fossil-derived
plastics to polymers derived from biomass and waste resources. While renewable …

Polyimide substrates used in flexible display devices need to withstand very high
temperatures and be highly thermally stable. The discovery of polyimides that satisfy these …

The recent successes of the Materials Genome Initiative have opened up new opportunities
for data-centric informatics approaches in several subfields of materials research, including …