Dresden 2020 –
wissenschaftliches Programm
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CPP 103: Topical Session: Data Driven Materials Science - Machine Learning for Materials Characterization (joint session MM/CPP)
Donnerstag, 19. März 2020, 15:45–17:15, BAR 205
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15:45 |
CPP 103.1 |
Topical Talk:
Machine learning tools in analyticat transmission electron microscopy — •Cécile Hébert and Hui Chen
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16:15 |
CPP 103.2 |
Automatic semantic segmentation of Scanning Transmission Electron Microscopy (STEM) images using an unsupervised machine learning approach — •Ning Wang, Christoph Freysoldt, Christian Liebscher, and Jörg Neugebauer
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16:30 |
CPP 103.3 |
Bayesian models and machine-learning for NMR crystal structure determinations — •Edgar Albert Engel, Andrea Anelli, Albert Hofstetter, Federico Maria Paruzzo, Lyndon Emsley, and Michele Ceriotti
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16:45 |
CPP 103.4 |
Teaching machines to learn dynamics in NMR observables — •Arobendo Mondal, Karsten Reuter, and Christoph Scheurer
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17:00 |
CPP 103.5 |
Automatic Identification of Crystallographic Interfaces from Scanning Transmission Electron Microscopy Data by Artificial Intelligence — •Byung Chul Yeo, Christian H. Liebscher, Matthias Scheffler, and Luca Ghiringhelli
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