MM 20: Data Driven Material Science: Big Data and Workflows III
Tuesday, March 19, 2024, 10:15–13:00, C 243
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10:15 |
MM 20.1 |
Bulk and Surface Properties of cesium-telluride Photocathode Materials via High-Throughput Density Functional Theory Calculations — •Holger-Dietrich Saßnick and Caterina Cocchi
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10:30 |
MM 20.2 |
Thermodynamic and phonon properties of multi-alkali antimonides from density-functional theory and machine learning — •Julia Santana-Andreo, Holger-Dietrich Saßnick, and Caterina Cocchi
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10:45 |
MM 20.3 |
Energetic and electronic properties of K-Sb and Na-Sb binary crystals from high-throughput ab initio calculations — •Richard Schier, Holger-Dietrich Saßnick, and Caterina Cocchi
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11:00 |
MM 20.4 |
Predicting Equilibrium Pressure for Hydrogen Storage: A Cheminformatics Approach Using Deep Neural Networks — •Sinan S. Faouri, Kai Sellschopp, Paul Jerabek, and Claudio Pistidda
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11:15 |
MM 20.5 |
Peeling back the layers; Incorporating Dispersion Interactions and Quantum Mechanics at Clay Mineral Interfaces. — •Sam Shepherd, Gareth. A Tribello, and David. M Wilkins
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11:30 |
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15 min. break
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11:45 |
MM 20.6 |
Experiment-driven atomistic materials modeling: Combining XPS and MLPs to infer the structure of a-COx — •Tigany Zarrouk and Miguel Caro
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12:00 |
MM 20.7 |
Exploring high-entropy alloy transport properties through the lens of machine learning — •Ruiwen Xie, Ye Wei, Bo Peng, Jiamu Liu, Liuliu Han, and Hongbin Zhang
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12:15 |
MM 20.8 |
The contribution has been moved to MM 45.2.
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12:30 |
MM 20.9 |
Influence of the neighboring phases of MnS inclusions on damage accumulation in case-hardening steel — •Clara Reinhart, Tom Reclik, Maximilian A. Wollenweber, Ulrich Kerzel, Talal Al-Samman, and Sandra Korte-Kerzel
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12:45 |
MM 20.10 |
High-throughput damage quantification in steel and opportunities for damage-controlled forming processes — •Maximilian A. Wollenweber, Jannik Gerlach, Tom Reclik, Clara Reinhart, Sebastian Münstermann, Talal Al-Samman, and Sandra Korte-Kerzel
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