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MM: Fachverband Metall- und Materialphysik

MM 53: Data Driven Material Science: Big Data and Workflows VI

Thursday, March 21, 2024, 10:15–13:00, C 243

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
10:30 MM 53.2 SE(3)-Transformers for predicting the electronic structure of hydrogen molecules — •Bartosz Brzoza and Attila Cangi
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
11:00 MM 53.4 Datadriven thermodynamic modeling with CALPHADTobias Spitaler and •Lorenz Romaner
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
  11:30 15 min. break
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
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
12:15 MM 53.8 Bayesian Optimization for High-Resolution Transmission Electron Microscopy — •Xiankang Tang, Yixuan Zhang, and Hongbin Zhang
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
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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