Artificial Intelligence for Materials Sciences
Welcome to the homepage of the AiMat (Artificial Intelligence for Materials Sciences) group at KIT in which we work on the development of AI and machine learning methods focusing on their application to materials science questions.
The research group headed by junior professor Pascal Friederich was established in 2020 and has been growing ever since. We are therefore always looking for talented students and researchers in computer or natural sciences to join our team!
Our main research areas are
- Data-driven property prediction and design of materials and molecules
- Accelerated, machine learning based atomistic simulations of materials and molecular systems
- Machine learning methods for autonomous data analysis and decision-making in self-driving labs
Today, the AiMat group recognizes the great science done by doctoral researchers and students: Marlen Neubert, Yuri Koide, Yumeng Zhang, Mariana Petrova, Annika Leinweber, Laura Ruple, Klara Eckhardt, Aleksandra Hryncyszyn, Michelle Walter, and many more over the previous years. Thanks also to Stephanie Wolf for providing great support. We are looking forward to welcoming more women into the group in the coming years!Learn more
Congratulations to Jonas Teufel for the Best Student Paper Award at the xAI World Conference 2023!
Jonas has given a talk entitled "MEGAN: Multi-Explanation Graph Attention Network" in which he presented the results of our recent homonymous arXiv preprint.
In the winter term 2023/24 we offer the lecture "Basics of Artificial Intelligence" together with the related exercises, a seminar on "Critical topics in AI" as well as a proseminar on "Advanced topics in machine learning".Check them now!
We are searching for talented and motivated young researchers with backgrounds in computer science or natural sciences to join the team! Currently available are two PhD projects and one postdoc position. Interested? Check out the job openings!
The GC-MAC Summer School 2023 will be held on 18-22 September at KIT. It will cover multiple aspects of materials acceleration platforms (MAPs) for energy materials, e.g. automated synthesis and characterization, integration of simulation methods in materials design, and ML methods for MAPs.Website & Registration