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 mate- rials 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. Aspects of practical relevance, e.g. concepts such as batch training, momentum, data augmentation will be discussed and used in hands-on implementations. The second part of this module is dedicated to the field of materials informatics and introduces concepts relevant for the digitization of materials science as well as for research data management (e.g., the concepts of metadata, ontologies, knowledge- graphs as well as semantic web technologies and the FAIR data principle). This will then be put into the context of accelerated materials design as well as data-driven materials development.
