Berlin 2024 –
scientific programme
MM 53: Data Driven Material Science: Big Data and Workflows VI
Thursday, March 21, 2024, 10:15–13:00, C 243
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10:15 |
MM 53.1 |
Scalable machine learning for predicting the electronic structure of matter — •Attila Cangi, Lenz Fiedler, Bartosz Brzoza, Karan Shah, Tim Callow, and Steve Schmerler
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10:30 |
MM 53.2 |
SE(3)-Transformers for predicting the electronic structure of hydrogen molecules — •Bartosz Brzoza and Attila Cangi
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10:45 |
MM 53.3 |
Physics-Informed Machine Learning for Addressing Challenges in Static and Time-Dependent Density Functional Theory — •Karan Shah and Attila Cangi
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11:00 |
MM 53.4 |
Datadriven thermodynamic modeling with CALPHAD — Tobias Spitaler and •Lorenz Romaner
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11:15 |
MM 53.5 |
Complete Basis Set Limit Extrapolation in Density Functional Theory Calculations using Statistical Learning — •Daniel Speckhard, Claudia Draxl, and Matthias Scheffler
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11:30 |
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15 min. break
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11:45 |
MM 53.6 |
High-resolution beyond the depth of field limit in 3D phase-contrast imaging using ptychographic multi-slice electron tomography — •Andrey Romanov, Min Gee Cho, Mary Cooper Scott, and Philipp Pelz
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12:00 |
MM 53.7 |
Advancing In-Situ SEM Imaging: Integrating Deep Learning Super-Resolution for Accelerated Analysis — •Tom Reclik, Philipp Schumacher, Setareh Medghalchi, Maximilian Wollenweber, Ulrich Kerzel, and Sandra Korte-Kerzel
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12:15 |
MM 53.8 |
Bayesian Optimization for High-Resolution Transmission Electron Microscopy — •Xiankang Tang, Yixuan Zhang, and Hongbin Zhang
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12:30 |
MM 53.9 |
Pydidas: A new tool for automated X-ray diffraction data analysis — •Malte Storm, Anton Davydok, Peter Staron, and Christina Krywka
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12:45 |
MM 53.10 |
Deep learning-based feature detection on 2D X-ray scattering data for high throughput data analysis — •Alexander Hinderhofer, Vladimir Starostin, Constantin Voelter, Alexander Gerlach, and Frank Schreiber
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