MM 34: Data Driven Materials Science: Interatomic Potentials / Reduced Dimensions
Donnerstag, 8. September 2022, 15:45–18:30, H45
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15:45 |
MM 34.1 |
Constructing Training Sets for Transferable Moment Tensor Potentials: Application to Defects in Bulk Mg — •Marvin Poul, Liam Huber, Erik Bitzek, and Joerg Neugebauer
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16:00 |
MM 34.2 |
The contribution has been withdrawn.
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16:15 |
MM 34.3 |
Take Two: Δ-Machine Learning for Molecular Co-Crystals — •Simon Wengert, Gábor Csányi, Karsten Reuter, and Johannes T. Margraf
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16:30 |
MM 34.4 |
Magnetic Atomic Cluster Expansion and application to Iron — •Matteo Rinaldi, Matous Mrovec, and Ralf Drautz
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16:45 |
MM 34.5 |
Kernel Charge Equilibration: Learning Charge Distributions in Materials and Molecules — •Martin Vondrak, Nikhil Bapat, Hendrik H. Heenen, Johannes T. Margraf, and Karsten Reuter
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17:00 |
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15 min. break
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17:15 |
MM 34.6 |
Machine Learning of ab-initio grain boundary Segregation Energies — •Christoph Dösinger, Daniel Scheiber, Oleg Peil, Vsevolod Razumovskiy, Alexander Reichmann, and Lorenz Romaner
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17:30 |
MM 34.7 |
Stability of binary precipitates in Cu-based alloys investigated through active learning and quantum computing — •Angel Diaz Carral, Xiang Xu, Azade Yazdan Yar, Siegfried Schmauder, and Maria Fyta
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17:45 |
MM 34.8 |
How to teach my deep generative model to create new RuO2 surface structures? — •Patricia König, Hanna Türk, Yonghyuk Lee, Chiara Panosetti, Christoph Scheurer, and Karsten Reuter
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18:00 |
MM 34.9 |
Data-Driven Design of Two-Dimensional Non-van der Waals Materials — •Rico Friedrich, Mahdi Ghorbani-Asl, Stefano Curtarolo, and Arkady V. Krasheninnikov
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18:15 |
MM 34.10 |
Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning — •Andreas Leitherer, Angelo Ziletti, and Luca M. Ghiringhelli
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