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
Flyer of the AiMat Summer School on Machine Learning for Materials 2025.
AiMat Summer School 2025

The Summer School will be held on 8-12 September 2025 at KIT. It will cover multiple applications of machine learning in materials research, e.g. materials design and property prediction, ML-potentials for atomistic simulations, self-driving labs, and materials synthesis predictions.

Website & Application
Photos of Jonas, Marlen, Jana and Navid outdoors, all smiling at the camera, in different natural settings.AiMat group
AiMat Experience

We are a diverse international research group with a broad range of scientific backgrounds ranging from computer science to chemistry and physics. You can discover our stories and find out why we joined AiMat and what we like about being part of the group in this interview series.

Read the interviews
AiMat logo with text "We are hiring!"
Join us!

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!

 

Job openings
A gloved hand manipulates samples with tweezers on a lab bench with test tubes.Kurt Fuchs/HI ERN
Science publication: Inverse Design of perovskite solar cells using AI

Together with our cooperation partners, we published a closed-loop workflow for the discovery of new organic molecules to increase the efficiency of perovskite solar cells. Our strategy combines high-throughput synthesis of organic semiconductors to create large datasets with Bayesian optimization methods and can be transferred to other areas of materials research, such as the search for new battery materials.

Read the KIT news
Photo of the Girls' day group in 2025 standing in front of a screen with the Girls' Day and AiMat logos.
Girls' Day 2025 at KIT

On April 3rd 2025, 10 schoolgirls visited our group to try out our new VR application. The use of VR goggles and haptic gloves allowed them to not only see but also “touch” molecules and thus to interactively explore the fascinating world of molecules from a completely new perspective.

More information
Blackboard with the word 'Knowledge' and arrows pointing in different directions.
Courses for the summer term 2025

In the summer term 2025 we offer the lecture "Machine learning for natural sciences" together with the related exercises, as well as a seminar on "Critical topics in AI".

Check them now!
 Flyer of the panel discussion "ML - hype or future?".
Panel discussion "ML - Hype or Future?"

Lately, everyone has been talking about AI. But what influence do AI and machine learning methods have in the natural sciences? Will they have a lasting impact on scientific research or will the hype create a bubble that will eventually burst? As part of our CZS Summer School, we would like to discuss these and other questions with our speakers and then engage in conversation with the audience in an informal get-together.

See the KIT event calendar entry here
Flyer of the CZS Summer School on Machine Learning for Chemistry 2024.
CZS Summer School 2024

The Summer School will be held on 9-13 September 2024 at KIT. It will cover multiple applications of machine learning in chemistry, e.g. molecular design and property prediction, ML-potentials for atomistic simulations, self-driving labs, and molecular synthesis predictions.

Website
Photo of the Girls' day group in 2024 in a library setting, one girl wearing a VR headset and gloves, others sitting around.
Girls' Day 2024 at KIT

On April 25th, 10 schoolgirls visited our group to try out our new VR application. The use of VR goggles and haptic gloves allowed them to not only see but also “touch” molecules and thus to interactively explore the fascinating world of molecules from a completely new perspective.

More information
Photo of the AiMat group in October 2023 standing by a lake with trees in the background.
International Day of Women and Girls in Science

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
Photo of Jonas Teufel holding his framed certificate and smiling.
Best Student Paper Award for Jonas

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.

arXiv:2211.13236v2
Flyer of the GC-MAC Summer School on materials acceleration platforms 2023.
GC-MAC Summerschool

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