The polyionic glass under investigation consists of a combination of oxides, fluorides, sulphates, phosphates and chlorides. The observable material properties result from interactions of this multitude of chemical components. Due to the complexity, however, statements about the structure and spatial arrangement of the basic elements are only possible to a very limited extent, so that the resulting properties are hardly predictable. Instead of time-consuming laboratory experiments, optimal compositions can now be identified with the help of analytical models. "We were also able to show that the ‘genes’ we found now fit very well with the little we know about the structure of these glasses from spectroscopic investigations," Wondraczek sums up.
In the tradition of Otto Schott
In their 'genome analysis' of glass, the Jena team is continuing methodological approaches that were first introduced in Jena 130 years ago by Otto Schott, the pioneer of glass research. "Schott was the first to develop modern glasses through systematic variations in chemical composition. From his observations, he derived correlations between chemistry and practical properties," says Wondraczek. Together with physicist Adolf Winkelmann, at that time a professor at the University of Jena, Schott translated these findings into mathematical regression models, in some ways a precursor to what researchers use today as part of 'machine learning'.
Pan Z, Dellith J, Wondraczek L, Genome mining in glass chemistry using linear component analysis of ion conductivity data, „Advanced Science“ (2023), DOI: 10.1002/advs.202301435External link