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Dresden 2017 – scientific program

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MM: Fachverband Metall- und Materialphysik

MM 60: Topical session: Data driven materials design - machine learning

Thursday, March 23, 2017, 12:00–13:15, BAR 205

12:00 MM 60.1 Finding descriptors for material properties from billions of candidates via compressed sensing: accurate prediction of crystal structures and band gaps from only chemical composition — •Runhai Ouyang, Emre Ahmetcik, Luca M. Ghiringhelli, and Matthias Scheffler
12:15 MM 60.2 Representing energy landscapes by combining neural networks and the empirical valence bond method — •Sinja Klees, Ramona Ufer, Volodymyr Sergiievskyi, Eckhard Spohr, and Jörg Behler
12:30 MM 60.3 Automatic crystal-structure classification using X-ray diffraction patterns and convolutional neural networks — •Angelo Ziletti, Matthias Scheffler, and Luca M. Ghiringhelli
12:45 MM 60.4 Optimizing Materials Properties with Machine Learning Techniques: A Case Study on Hard-Magnetic Phases — •Johannes J. Möller, Georg Krugel, Wolfgang Körner, Daniel F. Urban, and Christian Elsässer
13:00 MM 60.5 A theoretical tool to predict the nature of the 4f states of Ce compounds — •Heike C. Herper, Tofiq Ahmed, John M. Wills, Igor di Marco, Inka Locht, Anna Delin, Alexander V. Balasky, and Olle Eriksson
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