SDL Material Design presents

Machine Learning for Materials Science

Date: 23.10 – 25.10.2023 (9:00 – 17:00), Format: Hybrid

Short abstract:

Data Science and Machine Learning have been seen as the “Forth Paradigm” in Material Science and are reshaping the research direction in many areas. In this training,  the students/participates will gain an overview and hand-on experience on the most relevant machine learning algorithms for theoretical simulations, experimental characterization, and in general statistical analysis in materials science. The participants are able to work with available packages within Python to develop their own simple machine learning based programs, and are going to tackle a challenging project.  Though exemplary datasets, the participants will practice to apply appropriate methods to basic materials science problems, in particular Machine Learning assisted image segmentation, Machine-learning interatomic potentials, microstructure-property correlation analysis, and data-driven multiscale modeling.

Registration: Coming soon

Language: English

Capacity:  30 in Person, unlimited online

Further information: Own Computer with Python and Jupyternotebook installed. Alternatively GoogleColab can be used.

Materials: To be annouced for download

Responsible persons

Prof. Dr. Bai-Xiang Xu

TU Darmstadt

Prof. Dr. Karsten Albe

TU Darmstadt

Contact person

Binbin Lin

TU Darmstadt