Machine Learning for Materials 2025

With the topic “Machine Learning for Materials", this Summer School 2025 will cover multiple aspects of this young interdisciplinary research area:

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

We want to address young researchers at early career stages specifically, i.e. undergraduate students in informatics, chemistry and the material sciences as well as first and second year PhD students. 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?".

The organizing team (Tobias Schlöder and Pascal Friederich) is happy to assist and answer any questions – don’t hesitate to contact us for more information.

Location and Time

The Summer school will be held in Karlsruhe, Germany from 8 to 12 September 2025.

Program (speakers tba soon)

Monday (Materials property prediction)

Tuesday (ML potentials for materials simulations)

Wednesday (Graph neural networks)

Thursday (Self­-driving labs)

Friday (Generative models and LLMs)

Registration

Interested candidates are invited to submit an application through this registration link. If/once approved, a link for final information and payment will be provided.

Participants fee

The participation fee covers organization, local support, lunch and drinks during the day, the lab visit, the guided tour at ZKM, and the social dinner. Transport to and from Karlsruhe, as well as accommodation, needs to be individually organized and paid for by the participants.
We will offer reduced prices for undergraduates and PhD students a well as an early bird discount available until June 4th.

Funding

The Summer School Machine Learning for Chemistry is funded by the Carl-Zeiss-Stiftung with additional financial support by the KIT centers MaTeLiS and KCIST.