Machine Learning for Chemistry 2024

With the topic “Machine Learning for Chemistry", this CZS Summer School 2024 will cover multiple aspects of this young interdisciplinary research area:

  • Molecular descriptors and ML based molecular property prediction
  • Graph neural networks for molecules
  • Atomistic simulations enabled by machine-learned potentials
  • Molecular synthesis prediction
  • Self-driving labs in chemistry research

We want to specifically address young researchers at early career stages, 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, a poster session in which the participants can present their own research, and also a public evening event with a panel discussion on the impact of AI on research. Along with the scientific program, we will organize a side program consisting of a trip to Heidelberg including a visit of the castle, a social dinner as well as a public evening event.

The organizing team is happy to assist and answer any questions – don’t hesitate to contact Tobias Schlöder (tobias.schloeder∂kit.edu) or Pascal Friederich (pascal.friederich∂kit.edu) for more information.

Location and Time

The Summer school will be held in Karlsruhe, Germany from 9 to 13 September 2024.

Program and speakers

Monday (Molecular property prediction)

  • Geemi Wellawatte (EPFL, Switzerland)
  • Benjamin Sanchez-Lengeling (Google Deepmind, USA)

Tuesday (ML potentials for molecular simulations)

  • Pavlo Dral (Xiamen University, China)
  • Stefan Chmiela (TU Berlin, Germany)

Wednesday (Graph neural networks)

  • Jian Tang (Mila Québec, Canada)
  • Stephan Günnemann (TU Munich, Germany)

Thursday (Chemical reactions and synthesis predictions)

  • Jörg Behler (RU Bochum, Germany)
  • Marwin Segler (Microsoft Research AI4Science, UK)

Friday (Self-driving labs and research data)

  • Nicole Jung (KIT, Germany)
  • Pascal Friederich (KIT, Germany)

Registration

Interested candidates are invited to submit an application form to attend through this registration link. If/once the application is approved, more information including a link for payment will be provided.

Participants fee

The participation fee covers organization, local support, lunch and drinks during the day, the lab visit, a trip to Heidelberg, 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 13th.

  Early bird fee Regular fee
Undergraduates 100 € 160 €
PhD students 160 € 240 €
Others 300 € 420 €

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.