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MM: Fachverband Metall- und Materialphysik

MM 4: Data Driven Material Science: Big Data and Workflows I

Monday, March 18, 2024, 10:15–13:00, C 243

10:15 MM 4.1 Investigating Structural Descriptors for High-Dimensional Neural Network Potentials — •Moritz R. Schäfer, Moritz Gubler, Stefan Goedecker, and Jörg Behler
10:30 MM 4.2 Universally Accurate or Specifically Inadequate? Stress-Testing General Purpose Machine Learning Interatomic Potentials — •Konstantin Jakob, Karsten Reuter, and Johannes T. Margraf
10:45 MM 4.3 Pressure-transferable neural network models for density-functional theory — •Timothy Callow, Lenz Fiedler, Normand Modine, and Attila Cangi
11:00 MM 4.4 Towards Multi-Fidelity Machine Learning Using Robust Density Functional Tight Binding Models — •Mengnan Cui, Karsten Reuter, and Johannes T. Margraf
11:15 MM 4.5 Application of Question Answering method to extract information from materials science literature — •Matilda Sipilä, Farrokh Mehryary, Emil Nuutinen, Sampo Pyysalo, Filip Ginter, and Milica Todorović
  11:30 15 min. break
11:45 MM 4.6 Transferable interatomic potential of water with the atomic cluster expansion — •Eslam Ibrahim, Yury Lysogorskiy, and Ralf Drautz
12:00 MM 4.7 Automatic extraction and analysis of dislocations in atom probe tomography data using skeletonization — •Alaukik Saxena, Baptiste Gault, and Christoph Freysoldt
12:15 MM 4.8 Stable diffusion based microstructure reconstruction and generation — •Yixuan Zhang, Teng Long, and Hongbin Zhang
12:30 MM 4.9 Automatic Generation of Atomic Structure Datasets for Machine Learning Potentials: Alloys and Applicatoin to Mg/Al/Ca — •Marvin Poul, Liam Huber, and Joerg Neugebauer
12:45 MM 4.10 Physics-informed neural network for predicting the Gibbs free energy — •Clement Paulson, Amin Sakic, Vedant Dave, Elmar Rueckert, Ronald Schnitzer, and David Holec
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