HYBRID: From Quantum Mechanics to Self-Driving Labs: Accelerating Materials Discovery

This lecture will be held in presence in the Anna-von-Helmholtz-Bau of the Physikalisch-Technische Bundesanstalt in Berlin-Charlottenburg and can be followed online at the same time. Please use the links above to register your personal participation or to receive the access data for online participation.

Topic:

What if we could predict the next breakthrough material before it is ever made? Advances in quantum-mechanical simulations and machine learning now enable researchers to predict the properties of complex materials with unprecedented accuracy. Combined with automated, self-driving laboratories and digital research workflows, these tools can transform materials development from a slow, trial-and-error process into a rapid, data-driven discovery journey. This talk explores how quantum mechanics, machine learning, and automation are reshaping materials research, accelerating innovation, reducing resource consumption, and opening new pathways toward sustainable technologies.

CV:

Janine George is Head of the Division Digital Materials Chemistry at BAM Berlin and Professor of Materials Informatics at Friedrich Schiller University Jena. Her research combines quantum chemistry, machine learning, and condensed matter physics to accelerate the discovery of advanced materials. She earned her PhD from RWTH Aachen University and conducted postdoctoral research at Université catholique de Louvain as a Marie Skłodowska-Curie Fellow. From 2021 to 2025, she led a junior research group at BAM. She is a Werner von Siemens Fellow (2023) and ERC Starting Grant awardee (2024), and contributes to open-source materials science software including atomate2, pymatgen, LobsterPy, and autoplex.

Following the lecture, there will be a get-together where participants can exchange ideas with each other over pizza and drinks.

The event is sponsored by the Wilhelm and Else Heraeus Foundation.