Berlin 2024 –
scientific programme
MM 4: Data Driven Material Science: Big Data and Workflows I
Monday, March 18, 2024, 10:15–13:00, C 243
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10:15 |
MM 4.1 |
Investigating Structural Descriptors for High-Dimensional Neural Network Potentials — •Moritz R. Schäfer, Moritz Gubler, Stefan Goedecker, and Jörg Behler
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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
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10:45 |
MM 4.3 |
Pressure-transferable neural network models for density-functional theory — •Timothy Callow, Lenz Fiedler, Normand Modine, and Attila Cangi
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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
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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
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11:30 |
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15 min. break
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11:45 |
MM 4.6 |
Transferable interatomic potential of water with the atomic cluster expansion — •Eslam Ibrahim, Yury Lysogorskiy, and Ralf Drautz
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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
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12:15 |
MM 4.8 |
Stable diffusion based microstructure reconstruction and generation — •Yixuan Zhang, Teng Long, and Hongbin Zhang
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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
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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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