Machine Learning for Molecules

• Materials representations and ML­-based materials property prediction
• Atomistic simulations enabled by machine­-learned potentials
• Graph neural networks and inverse molecular design
• Self­-driving labs in chemistry
• LLMs and synthesis prediction

The program will contain both lecture-type presentations as well as interactive formats such as a lab visit, hands-on tutorials, and a poster session in which the participants can present their own research. Along with the scientific program, we will organize a side program consisting of a visit to ZKM (Center for Art and Media Karlsruhe) including a guided tour, a social dinner as well as a public evening including a panel discussion on the topic "Large Language Models in Science - Hype or Future?".