SKM 2023 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 31: Data Driven Materials Science: Big Data and Work Flows – Machine Learning

Mittwoch, 29. März 2023, 15:45–18:30, SCH A 251

15:45 MM 31.1 Neural networks trained on synthetically generated crystals can classify space groups of ICSD powder X-ray diffractograms — •Henrik Schopmans, Patrick Reiser, and Pascal Friederich
16:00 MM 31.2 Critical Assessment of Uncertainty Estimates of Machine- Learning Potentials — •Shuaihua Lu, Luca M. Ghiringhelli, Christian Carbogno, and Matthias Scheffler
16:15 MM 31.3 Learning to Spell Materials - Coordinate-free Discovery with Natural Language Processing — •Konstantin Jakob, Karsten Reuter, and Johannes T. Margraf
16:30 MM 31.4 Exploring materials dataspaces by combining supervised and unsupervised machine learning — •Andreas Leitherer, Angelo Ziletti, Christian H. Liebscher, Timofey Frolov, and Luca M. Ghiringhelli
  16:45 MM 31.5 The contribution has been withdrawn.
  17:00 15 min. break
17:15 MM 31.6 Accelerating the Search for High-Performance, Novel Materials with Active LearningThomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli, and •Christian Carbogno
17:30 MM 31.7 Machine learning discovery of new materials — •Jonathan Schmidt, Hai-Chen Wang, Noah Hoffman, Tiago Cerqueira, Pedro Borlido, Pedro Carrico, Love Pettersson, Claudio Verdozzi, Silvana Botti, and Miguel Marques
17:45 MM 31.8 Data-driven magneto-elastic interatomic potentials for discovering novel phases of transition metal alloys — •Mani Lokamani, Kushal Ramakrishna, Julian Tranchida, Svetoslav Nikolov, Hossein Tahmasbi, Michael Wood, and Attila Cangi
18:00 MM 31.9 FAIR Modelling Recipes for High-Throughput Screening of Metal Hydrides — •Kai Sellschopp, Philipp Zschumme, Michael Selzer, Claudio Pistidda, and Paul Jerabek
18:15 MM 31.10 Take Two: Δ-Machine Learning for Molecular Co-Crystals — •Simon Wengert, Gábor Csányi, Karsten Reuter, and Johannes Theo Margraf
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