WW-Colloquium: Prof. Dr. Norbert Huber – Machine learning in materials science and engineering – best practice, perspectives and pitfalls

Prof. Dr. Norbert Huber
Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin and Institute of Materials Physics and Technology, Hamburg University of Technology
Machine learning in materials science and engineering – best practice, perspectives and pitfalls

Machine learning (ML) is increasingly utilized to support the data driven analysis of relationships in multidimensional parameter spaces, ideally as an entry point for a more general phenomenological or physics-based model development. Applications include both forward and inverse problems as well as forward problems, for example parameter identification or modeling of structure-property relationships.
The talk will give an overview over a variety of solutions that benefit from the capability of artificial neural networks to approximate and interpolate complex relationships that are represented by a set of sparse data. The reason behind is that numerical simulations as well as experiments do often not allow to generate enough data such that the data set is not sufficient for a deep-learning approach in connection with the complexity of the problem at hand.
After a short introduction to artificial neural networks along with recommendations for data generation and feature engineering, the talk will cover a range of examples from nanoindentation and material parameter identification, the improvement of characterization techniques by ML correction methods towards recent problems in the prediction of structure-property relationships for materials with complex microstructure.
All these examples have in common that a successful ML model typically requires a comprehensive understanding of existing knowledge, expertise in translating this knowledge into meaningful input features, a compact ML architecture, and robust validation of the trained model. The talk will conclude with the example of nanoporous metals that demonstrates the importance of high-quality and bias-free data for the applicability and trustworthiness of the trained model, also emphasizing the need for a culture of open data, specifically towards curated data sets for training and validation of ML models.