teaching

We are offering master courses in the fields of materials data science and informatics and mechanics within the Chair of Data Science and Computer Science in Materials Science at RWTH Aachen University .

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Introduction to Data Mining and Machine Learning

Data mining and data analysis plays a growing role for the extraction of information both from simulations and experiments/microscopy. This requires knowledge of the algorithms and methods, the underlying statistical/probabilistic concepts as well as the ability to practically use such methods, e.g. through programming.

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Materials Data Science and Informatics

This module focuses on methods and practical implementations for using data science approaches to extract new information and knowledge from situations of materials scientific relevance. Used methods ranges from dimensionality reduction methods through various deep learning architectures (drop-out, convolutional networks, autoencoder, recurrent and generative adversarial networks) to approaches for high- throughput data analyze (e.g., image data from microscopy) or the use of tailored machine learning methods for predicting materials with new properties.

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From Molecular to Continnum Physics I

This module delves into the atomic structure of materials and its effect on mechanical properties. The focus will be put on crystalline materials, lattice defects therein, as well as their dynamics and contribution to the formation of microstructure. Relevant concepts from thermodynamics and statistical mechanics, as well as state of the art numerical simulation techniques, such as molecular dynamics (MD) and the phase-field method, are introduced.

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Last Modified: 18.08.2023