Machine Learning for Materials sciences (ML4Mat) Summer School

The topic of "Machine Learning for Material Science" is a young, rapidly growing field of research. There are still many open research questions at the interdisciplinary interface between the development of machine learning methods in computer science and their applications in materials science and chemistry.

The main research directions are the development of machine learning methods for the prediction of material and molecular properties, for accelerated atomistic simulations and for self-learning laboratories.

These research areas can only be solved through interdisciplinary collaboration between researchers in computer science and materials science. A joint summer school promotes understanding of the respective methods, issues and challenges. It thus contributes to the development of an interdisciplinary self-image of the young researchers.