Regensburg 2019 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 33: Topical session (Symposium MM): Big Data Analytics in Materials Science

Donnerstag, 4. April 2019, 10:15–13:15, H43

Sessions: Big Data Analytics in Materials Science I and II

	10:15	MM 33.1	Topical Talk: Supervised and unsupervised learning from the large body of materials literature — •Gerbrand Ceder
	10:45	MM 33.2	Reproducible massive calculations and data sharing with AiiDA and the Materials Cloud — •Giovanni Pizzi, Leopold Talirz, Snehal Kumbhar, Aliaksandr Yakutovich, Elsa Passaro, Marco Borelli, Sebastiaan P. Huber, Martin Uhrin, Spyros Zoupanos, Fernando Gargiulo, Ole Schuett, Joost VandeVondele, Thomas C. Schulthess, Berend Smit, and Nicola Marzari
	11:00	MM 33.3	The NOMAD 2018 Kaggle Competition: Tackling Materials-Science Challenges through Crowd Sourcing — •Christopher Sutton, Luca M. Ghiringhelli, Takenori Yamamoto, Xiangyue Liu, Angelo Ziletti, and Matthias Scheffler
	11:15	MM 33.4	Symmetry-invariant basis representations for machine- learning of electronic structure data beyond energies — •Michael Luya and Reinhard J. Maurer
	11:30		15 min. break
	11:45	MM 33.5	Topical Talk: Extending high-throughput materials discovery to finite temperatures: Concepts and application — •Tilmann Hickel, Janssen Jan, Halil Sözen, Fritz Körmann, Sudarsan Surendralal, Mira Todorova, Yury Lysogorskiy, Ralf Drautz, and Jörg Neugebauer
	12:15	MM 33.6	Crystal-structure identification in polycrystals via Bayesian deep learning — •Angelo Ziletti, Andreas Leitherer, Matthias Scheffler, and Luca Ghiringhelli
	12:30	MM 33.7	Neural-network representation of materials for robust crystal-structure recognition — •Andreas Leitherer, Angelo Ziletti, Matthias Scheffler, and Luca M. Ghiringhelli
	12:45	MM 33.8	Artificial intelligence in materials science: towards optimal descriptors — •Benedikt Hoock, Santiago Rigamonti, Luca Ghiringhelli, Matthias Scheffler, and Claudia Draxl
	13:00	MM 33.9	Global sensitivity analysis and surrogate modeling for materials models with rapid local variations — Juan Lorenzi, Sandra Döpking, and •Sebastian Matera