MM 9: Topical Session: Physics-driven Artificial Intelligence for Materials II
Montag, 9. März 2026, 15:45–18:30, SCH/A251
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15:45 |
MM 9.1 |
Topical Talk:
Atomistic simulations in the ternary Fe-O-H system: interatomic potential development and applications — •Baptiste Bienvenu, Mira Todorova, Matous Mrovec, Ralf Drautz, Dierk Raabe, and Jörg Neugebauer
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16:15 |
MM 9.2 |
Learning long-range interactions with equivariant charges — •Marcel F. Langer, Egor Rumiantsev, Tulga-Erdene Sodjargal, Michele Ceriotti, and Philip Loche
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16:30 |
MM 9.3 |
Physics-informed Hamiltonian-learning for large-scale electronic-structure calculations — •Martin Schwade, Shaoming Zhang, Frederik Vonhoff, Frederico P. Delgado, and David A. Egger
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16:45 |
MM 9.4 |
Making equivariant graph neural network prediction of electronic structure properties fast and accurate — •Chen Qian, Valdas Vitartas, James Kermode, and Reinhard J. Maurer
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17:00 |
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15 min. break
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17:15 |
MM 9.5 |
Predicting the Thermal Properties of Semiconductor Defects with Equivariant Neural Networks — •Jonas A. Oldenstaedt, Manuel Grumet, Xiangzhou Zhu, Patrick Rinke, and David A. Egger
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17:30 |
MM 9.6 |
Learning exact exchange with symbolic regression — •Noah Hoffmann, Santiago Rigamonti, and Claudia Draxl
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17:45 |
MM 9.7 |
Development of a GRACE Machine-Learning Potential for Modeling SiC Epitaxial Growth — •Anders Vesti, Thomas Hammerschmidt, and Ralf Drautz
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18:00 |
MM 9.8 |
Integrating FlashMD into LAMMPS for Efficient Long-Timestep Molecular Dynamics — •Johannes Spies, Filippo Bigi, and Michele Ceriotti
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18:15 |
MM 9.9 |
Learning to Converge: ML-based Initialization for Fast DFTB Simulations — •Maximilian L. Ach, Karsten Reuter, and Chiara Panosetti
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