Speaker
Niels Cautaerts
(Max-Planck-Institut für Eisenforschung)
Description
Transmission electron microscopy data is rich in quantitative information about materials, information that could in theory be coupled to atomistic simulations, but extracting and harnessing that information is non-trivial. Machine learning approaches may facilitate this, but these are hampered by the limited availability and interoperability of the data. In this talk we present approaches, progress, and challenges to making TEM data more F.A.I.R. (findable, accessible, interoperable and reusable) with the ultimate aim to bridge the gap between TEM, machine learning and atomistics.
Primary authors
Niels Cautaerts
(Max-Planck-Institut für Eisenforschung)
Dr
Christian Liebscher
(Max-Planck-Institut für Eisenforschung)
Prof.
Christoph Koch
(Humboldt university of Berlin)
Prof.
Gerhard Dehm
(Max-Planck-Institut für Eisenforschung)
