research
These are our research interests.
We work on research projects that aim at an accelerated materials design at BAM and the University of Jena. We primarily focus on the safety and sustainability of materials. Together with our colleagues at BAM, we aim at combining our efforts with Materials Acceleration Platforms (i.e., automated platforms for materials searches/optimizations). Additionally, chemically complex materials are one of our joint interests with colleagues at BAM.
New chemical heuristics
The group works to test and develop chemical heuristics (intuitive rules) to advance knowledge and understanding in solid-state chemistry and physics, using geometric and quantum chemical descriptors for chemical environments. For the latter, the group is developing open-source tools for automated bond analysis (LobsterPy, atomate2)
High-throughput calculations and automations
Large amounts of reliably calculated material data are necessary to develop new chemical heuristics. Here, the group develops workflows and methods for high-throughput calculations to reliably perform such calculations. Among other things, the group regularly contributes its own developments to well-known open-source material informatics codes such as Pymatgen, and Atomate2 and has extensive expertise with these codes. Because of these activities, Janine is part of the Materials Project Software Foundation.
Machine Learning Potentials
Together with our collaboration partners (group of Prof. Volker Deringer) at the University of Oxford, we develop the software autoplex to automate the training and evaluation of machine learned interatomic potentials. Together with the group of Prof. Silvana Botti at Ruhr University Bochum, we work on finding new chemically complex oxides.
Vibrational Properties
Vibration properties play a fundamental role in the stability and heat transport of materials. Both material properties are significant for the safe use of materials. Here, the group is concerned with the ab initio prediction of such data to advance machine learning of such properties.
5 most important recent publications with contributions from our lab
- “Accelerated data-driven materials science with the Materials Project”
- “An automated framework for exploring and learning potential-energy surfaces”
- “Atomate2: Modular workflows for materials science”
- “A Quantum-Chemical Bonding Database for Solid-State Materials”
- “A foundation model for atomistic materials chemistry”