Dresden 2026 –
wissenschaftliches Programm
MM 17: Data-driven Materials Science: Big Data and Workflows II
Dienstag, 10. März 2026, 14:00–15:45, SCH/A251
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14:00 |
MM 17.1 |
Modelling Diffusion Kinetics in Refractory High Entropy Alloys Using Graph Neural Network Database Models — •Klemens Lechner, Jiyao Zhang, Peter Wagatha, Wolfram Knabl, Helmut Clemens, and David Holec
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14:15 |
MM 17.2 |
Broken neural scaling laws in machine learning for optical properties of metals — •Max Großmann, Marc Thieme, Malte Grunert, and Erich Runge
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14:30 |
MM 17.3 |
Simultaneous Learning of Static and Dynamic Charges — Philipp Stärk, •Philip Loche, Marcel Langer, Henrik Stooß, Michele Ceriotti, and Alexander Schlaich
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14:45 |
MM 17.4 |
A high-throughput study of heterostructures with polar discontinuities — •Maria Andolfatto, Junfeng Qiao, Davide Campi, and Nicola Marzari
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15:00 |
MM 17.5 |
Leveraging Koopmans band structure for exciton characterization in materials — •Miki Bonacci, Nicola Colonna, Edward Linscott, and Nicola Marzari
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15:15 |
MM 17.6 |
Many-body perturbation theory vs. density functional theory: A systematic benchmark for band gaps of solids — •Marc Thieme, Max Großmann, Malte Grunert, and Erich Runge
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15:30 |
MM 17.7 |
Learning G0W0 Self-Energies in Real Space with Equivariant Neural Networks — •Elisabeth Keller, Karsten W. Jacobsen, and Kristian S. Thygesen
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