Christopher Künneth

In 2018, Chris passed his PhD with highest distinction (summa cum laude) at the Technical University of Munich. Chris' research during his PhD was focused on identifying the underlying causes of pyroelectricity and ferroelectricity in HfO2 and ZrO2, using computational techniques such as density functional theory and molecular dynamics. Following graduation, Chris was granted the prestigious Feodor Lynen fellowship for postdoctoral researchers by the Alexander von Humboldt Foundation. He began this fellowship in February 2019 in the Ramprasad Group at the Georgia Institute of Technology in Atlanta, USA. During his postdoctoral research, Chris concentrated on applying machine learning methods in the field of materials science (materials informatics) with a special focus on polymeric materials. Specifically, he created fast and accurate machine learning pipelines for predicting properties of polymers or designing new polymers that satisfy specific property requirements. These machine learning models are deployed at the Polymer Genome project website.

Chris joined the University of Bayreuth (UBT) as Assistant Professor in March 2023. The Kuenneth group is dedicated to democratizing and streamlining machine learning in materials science. Such materials informatics efforts are underway more vigorously than ever before to efficiently and effectively discover, design, and develop new materials that meet specific application needs. To contribute to this exciting field, the Kuenneth group primarily concentrates on two research directions: (i) advancing cutting-edge machine learning techniques in materials science, and (ii) utilizing these techniques to study a diverse range of materials. A special focus of the Kuenneth group is sustainable and polymeric materials.