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MM: Fachverband Metall- und Materialphysik

MM 11: Data Driven Material Science: Big Data and Workflows II

Monday, March 18, 2024, 15:45–18:00, C 243

15:45 MM 11.1 Leveraging Multi-Fidelity Data In AI-Driven Sequential Learning of Materials Properties: Identifying Stable Water-Splitting Catalysts — •Akhil S. Nair, Lucas Foppa, and Matthias Scheffler
16:00 MM 11.2 From ab-initio to scattering experiments using neuroevolution potentials — •Eric Lindgren, Adam Jackson, Zheyong Fan, Christian Müller, Jan Swenson, Thomas Holm-Rod, and Paul Erhart
16:15 MM 11.3 Multi-Objective Optimization of Subgroups for the Discovery of Exceptional Materials — •Lucas Foppa and Matthias Scheffler
16:30 MM 11.4 From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery — •Lucas Foppa, Mario Boley, Felix Luong, Simon Teshuva, Daniel Schmidt, and Matthias Scheffler
  16:45 15 min. break
17:00 MM 11.5 A generic Bayesian Optimization framework for the inverse design of materials — •Zhiyuan Li, Yixuan Zhang, and Hongbin Zhang
17:15 MM 11.6 Uncertainty quantification by shallow ensemble propagation — •Matthias Kellner and Michele Ceriotti
  17:30 MM 11.7 The contribution has been withdrawn.
17:45 MM 11.8 Adaptive-precision potentials for large-scale atomistic simulations — •David Immel, Ralf Drautz, and Godehard Sutmann
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