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Exploring Data-Rich Materials Analytics with Machine Learning

Gemeinsames Kolloquium des Fachbereichs Physik und des SFB 1083

Veranstaltungsdaten

Link zur Videokonferenz

Abstract:

The heart of modern material science lies in the dualism of experiments and a plethora of theoretical models to explain them. The on-going, rapid growth of available data and the rise of machine-learning and artificial intelligence offer novel ways for doing scientific research, but also challenge the traditional model-based understanding. Using examples from scanning transmission electron microscopy (STEM) and atom probe tomography (APT), I will show how data-centric methods can be turned into tools that both require and deliver scientific insight.