MM 11: Data Driven Material Science: Big Data and Workflows II
Montag, 18. März 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The contribution has been withdrawn.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
|
|
|