Dresden 2020 –
wissenschaftliches Programm
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CPP 68: Topical Session: Data Driven Materials Science - Descriptors (joint session MM/CPP)
Mittwoch, 18. März 2020, 11:45–13:15, BAR 205
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11:45 |
CPP 68.1 |
Evaluating representations of atomistic systems for machine learning — •Marcel Langer and Matthias Rupp
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12:00 |
CPP 68.2 |
Information-theory-driven identification of compact descriptors for accurate machine-learning predictions — •Benjamin Regler, Matthias Scheffler, and Luca M. Ghiringhelli
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12:15 |
CPP 68.3 |
Size-Extensive Molecular Machine Learning with Global Descriptors — •Johannes Margraf, Hyunwook Jung, Sina Stocker, Christian Kunkel, and Karsten Reuter
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12:30 |
CPP 68.4 |
Hierarchical SISSO: predicting complex materials properties building on simpler ones — •Lucas Foppa, Sergey V. Levchenko, Matthias Scheffler, and Luca M. Ghiringhelli
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12:45 |
CPP 68.5 |
Similarity descriptors for data-driven materials science — •Martin Kuban, Santiago Rigamonti, and Claudia Draxl
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13:00 |
CPP 68.6 |
Machine-learning descriptors with domain knowledge of the interatomic bond — •Thomas Hammerschmidt, Jan Jenke, Aparna P.A. Subramanyam, Jörg Koßmann, Yury Lysogorskiy, and Ralf Drautz
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