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Vogiatzis Group Publishes in npj Computational Materials

Associate Professor of Chemistry Konstantinos Vogiatzis, in collaboration with Professor of Mathematics Vasileios Maroulas and Eastman Chemical Company, has published a new machine learning model for predicting the properties of new polymeric materials.

Polymers are everywhere. From cookware to medical devices, polymers have become important to modern life due in part to a growing list of potential uses, and desirable properties like high durability and resistance to corrosion.

Creating new polymers can be an expensive, time-consuming process. Because of this, researchers attempt to predict the future properties of polymers using a variety of tools. Computational prediction methods allow researchers to screen polymer combinations for the desired properties before beginning experimentation. However, finding ways to represent polymers as machine-readable inputs can be difficult, creating a challenge for developing accurate prediction models.

Vogiatzis’ team is attempting to tackle these challenges by creating a deep learning method to predict polymer properties called PolymerGNN. PolymerGNN relies on state-of-the-art graph neural networks (GNN) and machine learning to predict the properties of new polymers using a database of complex polyesters.

“Polyesters offer a diverse material space formed by considering many different types of multifunctional acids and glycols, which are the building blocks of these materials,” said Vogiatzis. This, coupled with other complex properties of polyesters, creates a large materials design space Vogiatzis and his team were able to leverage in the development of PolymerGNN.

Vogiatzis worked with Vasileios Maroulas and students Owen Queen, Dr. Gavin McCarver and Sai Thatigotla to develop the general framework and GNN-based machine learning model for PolymerGNN. Collaborators from Eastman Chemical Company synthesized a set of more than 240 polymers and helped compile a database of properties which was used to train PolymerGNN.

Once trained, PolymerGNN accurately predicted both glass transition temperature and intrinsic viscosity. Glass transition temperature is the temperature at which a polymer shifts between a hard state and a softened state. Intrinsic viscosity is a measurement of a polymer’s molecular weight, which can indicate the polymer’s melting point, crystallinity, and tensile strength. These properties are fundamental to the ultimate physical traits of a given polymer and are critical to the development of adhesives, plastics, and more.

Vogiatzis’ team recently published this work in npj Computational Materials, an open access journal from Nature Research. They have also released PolymerGNN as an open-source codebase. Vogiatzis and Maroulas have collaborated on previous machine learning projects published by the American Chemical Society and Nature Communications. Read the most recent publication here.