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 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 trip to Heidelberg including a visit of the castle, a social dinner as well as a public evening including a panel discussion on the topic "Machine Learning 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 9 to 13 September 2024.
Program and speakers
Monday (Molecular property prediction)
- Pascal Friederich (KIT, Germany)
- Mayk Caldas Ramos (University of Rochester, USA)
- 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, Self-driving labs)
- Jörg Behler (RU Bochum, Germany)
- Pascal Friederich (KIT, Germany)
Friday (Chemical reactions and synthesis predictions, Self-driving labs, research data)
- Marwin Segler (Microsoft Research AI4Science, UK)
- Nicole Jung (KIT, Germany)
- Mayk Caldas Ramos (University of Rochester, USA)
Registration
Attention! We have reached already the maximum number of participants. If you want to be added to our waiting list, don't hesitate to nevertheless submit an application to attend the summer school through this registration link. We will contact you as soon as we have new free places.
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.