Dresden 2020 –
wissenschaftliches Programm
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MM 26: Topical Session: Data Driven Materials Science - Machine Learning for Materials Properties
Dienstag, 17. März 2020, 14:15–15:30, BAR 205
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14:15 |
MM 26.1 |
From Atom Probe Tomography to CALPHAD modeling: Estimating Tc from local concentration fluctuations — •Marvin Poul, Sebastian Eich, and Guido Schmitz
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14:30 |
MM 26.2 |
Analysis of magnetic properties in the Fe-Si system using first principles calculations — •Matteo Rinaldi, Matous Mrovec, and Ralf Drautz
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14:45 |
MM 26.3 |
Machine learning modeling of magnetic ground state and Curie temperature — •Teng Long, Nuno Fortunato, Yixuan Zhang, Oliver Gutfleisch, and Hongbin Zhang
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15:00 |
MM 26.4 |
Automatization of magnetic properties calculation using AiiDA-FLEUR — •Vasily Tseplyaev, Jens Bröder, Daniel Wortmann, Markus Hoffmann, and Stefan Blügel
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15:15 |
MM 26.5 |
Screening impurity effects in topological insulators with the AiiDA-KKR plugin — •Philipp Rüßmann, Fabian Bertoldo, Phivos Mavropoulos, and Stefan Blügel
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