Bayesian optimization is an old technique revived by using machine learning models at its core. This enables a plethora of extensions that really make the technique a great match for several real world applications. We showcase several examples such as custom and chemical encodings, transfer learning and slot-based mixture modelling enabled by our open-source code BayBE (https://github.com/emdgroup/baybe). [more]

The need for improved functionalities in extreme environments is fueling interest in high-entropy ceramics. [more]

The basic transport properties of thermoelectrics (TEs) are governed by electron and phonon, as well as their interaction. The transport properties have been well-documented for more than half century. [more]

I present the implementation and application of the accurate, machine-learned aPBE0 functional and the exchange-hole dipole moment (XDM) dispersion correction within the FHI-aims code. [more]

My aim is to show how data, as well as physics based models, can be utilized in conjunction with data science to guide materials discovery. [more]