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Regensburg 2022 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 22: Data Driven Materials Science: Experimental Data Treatment and Machine Learning

Wednesday, September 7, 2022, 10:15–13:00, H46

10:15 MM 22.1 Topical Talk: Ingredients for effective computer-augmented experimental materials science — •Christoph T. Koch, Markus Kühbach, Sherjeel Shabih, Benedikt Haas, and Sandor Bockhauser
10:45 MM 22.2 A materials informatics framework to discover patterns in atom probe tomography data — •Alaukik Saxena, Nikita Polin, Baptiste Gault, Christoph Freysoldt, and Jörg Neugebauer
11:00 MM 22.3 Correcting density artifacts in Atom Probe reconstructions: A tip shape-corrected volume reconstruction approach — •Patrick Stender, Daniel Beinke, Felicitas Bürger, and Guido Schmitz
  11:15 MM 22.4 The contribution has been withdrawn.
  11:30 15 min. break
11:45 MM 22.5 Topical Talk: Physics guided machine learning tools in analytical transmission electron microscopy — •Cecile Hebert, Hui Chen, and Adrien Teurtrie
12:15 MM 22.6 Motif Extraction from Crystalline Images in Real Space — •Amel Shamseldeen Ali Alhassan and Benjamin Berkels
12:30 MM 22.7 Analysis of acoustic emission spectra for structural health monitoring — •Klaus Lutter, Viktor Fairuschin, and Thorsten Uphues
12:45 MM 22.8 Optimizing laser powder bed fusion by machine learning methods — •Dmitry Chernyavsky
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