Berlin 2024 –
scientific programme
MM 43: Data Driven Material Science: Big Data and Workflows V
Wednesday, March 20, 2024, 15:45–18:00, C 243
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15:45 |
MM 43.1 |
The MALA Package - Transferable and Scalable Electronic Structure Simulations Powered by Machine Learning — •Lenz Fiedler and Attila Cangi
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16:00 |
MM 43.2 |
A robust, simple and efficient algorithm to converge GW calculations — •Max Großmann, Malte Grunert, and Erich Runge
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16:15 |
MM 43.3 |
FAIR Data Management for Computational Materials Science using NOMAD — •Luca M. Ghiringhelli, Joseph F. Rudzinski, José M. Pizarro, Nathan Daelman, and Silvana Botti
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16:30 |
MM 43.4 |
A many-body framework for long-range interactions in atomistic machine learning — •Kevin Kazuki Huguenin-Dumittan, Philip Robin Loche, and Michele Ceriotti
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16:45 |
MM 43.5 |
Automated prediction of Fermi surfaces from first principles — •Nataliya Paulish, Junfeng Qiao, and Giovanni Pizzi
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17:00 |
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15 min. break
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17:15 |
MM 43.6 |
Accurate and Efficient Protocols for High-Throughput Computational Materials Science — •Gabriel M. Nascimento, Flaviano José dos Santos, Marnik Bercx, Davide Grassano, Giovanni Pizzi, and Nicola Marzari
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17:30 |
MM 43.7 |
Defect Phase Diagrams for Grain Boundaries in Mg: Automized workflows for chemical trends — •Prince Mathews, Rebecca Janisch, Jörg Neugebauer, and Tilmann Hickel
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17:45 |
MM 43.8 |
The contribution has been withdrawn.
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