MM 25: Data Driven Materials Science: Computational Frameworks / Chemical Complexity
Mittwoch, 7. September 2022, 15:45–18:30, H46
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15:45 |
MM 25.1 |
Topical Talk:
Automated atomistic calculation of thermodynamic and thermophysical data — •Jan Janssen, Tilmann Hickel, and Jörg Neugebauer
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16:15 |
MM 25.2 |
Efficient parameterization of the atomic cluster expansion — •Anton Bochkarev, Yury Lysogorskiy, Matous Mrovec, and Ralf Drautz
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16:30 |
MM 25.3 |
Atomic cluster expansion: a universal machine learning potential for magnesium — •Eslam Ibrahim, Yury Lysogorskiy, Matous Mrovec, and Ralf Drautz
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16:45 |
MM 25.4 |
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence — •Lucas Foppa, Christopher Sutton, Luca M. Ghiringhelli, Sandip De, Patricia Löser, Stephan Schunk, Ansgar Schäfer, and Matthias Scheffler
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17:00 |
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15 min. break
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17:15 |
MM 25.5 |
Topical Talk:
Understanding Dislocation Flow and Avalanches in High Entropy Alloys by Machine Learning-based Data Mining of In-Situ TEM Experiments — •Stefan Sandfeld
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17:45 |
MM 25.6 |
Phase stability and short range order in CrCoNi medium entropy alloy — •Sheuly Ghosh, Vadim Sotskov, Alexander Shapeev, Fritz Koermann, and Joerg Neugebauer
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18:00 |
MM 25.7 |
The contribution has been withdrawn.
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18:15 |
MM 25.8 |
Databases for Machine Learning of Grain Boundary Segregation — •Alexander Reichmann, Christoph Dösinger, Daniel Scheiber, Oleg Peil, Vsevolod Razumovskiy, and Lorenz Romaner
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