MM 60: Topical session: Data driven materials design - machine learning
Thursday, March 23, 2017, 12:00–13:15, BAR 205
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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
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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
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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
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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
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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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