Berlin 2024 –
scientific programme
MM 11: Data Driven Material Science: Big Data and Workflows II
Monday, March 18, 2024, 15:45–18:00, C 243
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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
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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
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16:15 |
MM 11.3 |
Multi-Objective Optimization of Subgroups for the Discovery of Exceptional Materials — •Lucas Foppa and Matthias Scheffler
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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
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16:45 |
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15 min. break
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17:00 |
MM 11.5 |
A generic Bayesian Optimization framework for the inverse design of materials — •Zhiyuan Li, Yixuan Zhang, and Hongbin Zhang
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17:15 |
MM 11.6 |
Uncertainty quantification by shallow ensemble propagation — •Matthias Kellner and Michele Ceriotti
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17:30 |
MM 11.7 |
The contribution has been withdrawn.
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17:45 |
MM 11.8 |
Adaptive-precision potentials for large-scale atomistic simulations — •David Immel, Ralf Drautz, and Godehard Sutmann
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