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June 2023

Archives for June 2023

Vogiatzis named Bodossaki Distinguished Young Scientist

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Konstantinos Vogiatzis, associate professor in the chemistry department, has been named a Bodossaki Distinguished Young Scientist Award winner. The award recognizes young Greek scientists for their work in a number of academic fields, including science, life sciences, applied science and technology, and the social sciences.

Vogiatzis’ work is centered on the development of computational methods based on electronic structure theory and artificial intelligence. He and his team apply this to chemical systems for clean, green technology.

“As an independent researcher, my work has focused on leveraging machine learning in computational chemistry, using modeling and simulation for the discovery of novel molecules and materials with enhanced properties,” said Vogiatzis. “The guiding objective of my research is to clarify the fundamental physical principles influencing the properties of molecules and materials through the interpretation of experimental data.”

Since 1993, the Bodossaki Foundation has distributed Distinguished Young Scientist Awards every two years. In that time, 57 Greek scientists have been recognized for outstanding research conducted across a global stage. Candidates for the Bodossaki Distinguished Young Scientist Award are nominated by peers, collaborators, and institutions in which they work. Vogiatzis was nominated by Vanda Glezakou, a colleague at Oak Ridge National Laboratory and fellow native of Greece.

Vogiatzis will attend a ceremony in Greece this summer where he will be presented with his award.As a Bodossaki honoree, Vogiatzis joins the ranks of Greek professors working at leading research institutions around the world, including Harvard University, the University of Oxford, and the University of Toronto.

“I would like to express my gratitude to the Bodossaki Foundation, both for recognizing my work and for the honor of being included among the outstanding scientists receiving these awards now and in years past,” said Vogiatzis. “This award is the result of a 17-year course of scientific study that began in the classrooms and research laboratories of Greek universities. This, however, is just the beginning and I look forward to many more years continuing the search for new discoveries in the field of chemistry.”

Vogiatzis joined the University of Tennessee, Knoxville in 2016. Since that time, he has authored more than 40 publications and mentored 15 graduate students. He is the recipient of the 2020 and 2022 Ffrancon Williams Endowed Faculty Award in Chemistry, the 2021 OpenEye Outstanding Junior Faculty Award presented by the American Chemical Society, and a 2021 NSF CAREER award.

Read more about the Bodossaki Foundation and the 2023 Distinguished Young Scientist awardees here.

 

Vogiatzis Group Publishes in npj Computational Materials

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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.