MM 22: Data Driven Materials Science: Experimental Data Treatment and Machine Learning
Wednesday, September 7, 2022, 10:15–13:00, H46
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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
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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
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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
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11:15 |
MM 22.4 |
The contribution has been withdrawn.
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11:30 |
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15 min. break
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11:45 |
MM 22.5 |
Topical Talk:
Physics guided machine learning tools in analytical transmission electron microscopy — •Cecile Hebert, Hui Chen, and Adrien Teurtrie
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12:15 |
MM 22.6 |
Motif Extraction from Crystalline Images in Real Space — •Amel Shamseldeen Ali Alhassan and Benjamin Berkels
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12:30 |
MM 22.7 |
Analysis of acoustic emission spectra for structural health monitoring — •Klaus Lutter, Viktor Fairuschin, and Thorsten Uphues
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12:45 |
MM 22.8 |
Optimizing laser powder bed fusion by machine learning methods — •Dmitry Chernyavsky
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