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MM: Fachverband Metall- und Materialphysik

MM 37: Topical session (Symposium MM): Big Data Analytics in Materials Science

Donnerstag, 4. April 2019, 15:00–18:45, H43

Sessions: Big Data Analytics in Materials Science III and IV

	15:00	MM 37.1	Topical Talk: Atomistic Machine Learning between Physics and Data — Michele Ceriotti and •Michael Willatt
	15:30	MM 37.2	Atomic cluster expansion for accurate and transferable interatomic potentials — •Ralf Drautz
	15:45	MM 37.3	Simultaneous and Reinforced Learning of Materials Properties from Incomplete Databases with Multi-Task SISSO — •Emre Ahmetcik, Runhai Ouyang, Christian Carbogno, Matthias Scheffler, and Luca M. Ghiringhelli
	16:00	MM 37.4	Information-theoretic Feature Selection and its Applications in Materials Science — •Benjamin Regler, Matthias Scheffler, and Luca M. Ghiringhelli
	16:15	MM 37.5	The speech of strangers in material science — •Juliana Schell, Peter Schaaf, Hans-Christian Hofsäss, and Doru C. Lupascu
	16:30		15 min. break
	16:45	MM 37.6	Topical Talk: High-throughput with Particle Technology — •Lutz Mädler
	17:15	MM 37.7	Topical Talk: Microstructure is the know-it-all - classification approaches with data mining and deep learning methods — •Frank Mücklich and Dominik Britz
	17:45	MM 37.8	Electronic density-of-states fingerprints for finding similar materials — •Martin Kuban, Santiago Rigamonti, Markus Scheidgen, and Claudia Draxl
	18:00	MM 37.9	Accurate Thermal Conductivities of Complex, Strongly-Anharmonic Solids — •Florian Knoop, Thomas Purcell, Marcel Hülsberg, Matthias Scheffler, and Christian Carbogno
	18:15	MM 37.10	Surface Structure Search using Coarse Grained Modeling and Bayesian Linear Regression — •Lukas Hörmann, Andreas Jeindl, Alexander T. Egger, and Oliver T. Hofmann
	18:30	MM 37.11	Predicting Reaction Energetics with Machine Learning — •Sina Stocker, Johannes T. Margraf, and Karsten Reuter