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?" (see below).

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 and speakers

Monday (Materials property prediction)

• Taylor D. Sparks (University of Utah)
• Kamal Choudhary (Johns Hopkins University, Baltimore)
• Pascal Friederich (KIT, Germany)

Tuesday (Self­-driving labs)

• Mario Krenn (University of Tübingen)
• Sterling Baird (University of Toronto)

Wednesday (Graph neural networks)

• Arghya Bhowmik (Technical University of Denmark)
• Ralf Drautz (Ruhr-Universität Bochum)

Thursday (ML potentials for materials simulations)

• Volker L. Deringer (Oxford University)
• Hanna Türk (École Polytechnique Fédérale de Lausanne)

Friday (Generative models and LLMs)

• Janine George (Bundesanstalt für Materialforschung und -prüfung, Berlin & Friedrich-Schiller-Universität Jena)
• Kevin Maik Jablonka (Friedrich-Schiller-Universität Jena)

Panel discussion

As part of the Summer School, we will organise a public panel discussion on Tuesday with the topic "LLMs in Science – hype or future?". Large Language Models are not only becoming a part of our daily lives but also of how we do science. However, it is very hard to estimate to what extent they will influence, accelerate, or even drive scientific progress in the future. We will discuss these and other questions with our panelists, followed by engaging in a conversation with the audience in an informal get-together with snacks and drinks.

Panelists

• Danni Liu (KIT, AI for Language Technology)
• Prof. Dr. Mario Krenn (University of Tübingen, LLMs in Science)
• Dr. Charlotte Debus (KIT, Robust and Efficient AI)

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.

Previous Summer Schools

This is our third Summer School. You can find a list of all Summer Schools organized by us here.