MM 22: Data-driven Materials Science: Big Data and Workflows III
Wednesday, March 11, 2026, 10:15–12:45, SCH/A216
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10:15 |
MM 22.1 |
Hashing It Out: Overcoming the Duplicate Structure Filtering Bottleneck for Large Data Sets — •Julian Holland, Juan Manuel Lombardi, Chiara Panosetti, and Karsten Reuter
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10:30 |
MM 22.2 |
MC3D: The Materials Cloud FAIR and full-provenance materials database — •Michail Minotakis
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10:45 |
MM 22.3 |
Building a FAIR Community around Parsing — •Nathan Daelman, Alvin N. Ladines, Esma Boydas, Martin Kuban, Bernadette Mohr, Sascha Klawohn, Rubel Mozumber, Christina Ertural, Silvana Botti, Joseph F. Rudzinski, Lauri Himanen, and FAIRmat Team
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11:00 |
MM 22.4 |
Uncertainty Propagation in Machine-learned Interatomic Potentials — •Haitham Gaafer, Jan Janssen, and Jörg Neugebauer
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11:15 |
MM 22.5 |
Accurately predicting thermal conductivity using non-equilibrium molecular dynamics simulations and machine-learned force fields — •Florian Unterkofler, Lukas Legenstein, Sandro Wieser, and Egbert Zojer
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11:30 |
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15 min. break
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11:45 |
MM 22.6 |
Data-efficient training of interatomic potentials using finite-temperature DFT structures — •Martin Schlipf, Sudarshan Vijay, and Georg Kresse
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12:00 |
MM 22.7 |
MACE-based Machine Learning Interatomic Potentials for Iron-Nickel Alloys: Validation Across Composition and Pressure Ranges — •Kushal Ramakrishna, Mani Lokamani, and Attila Cangi
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12:15 |
MM 22.8 |
Benchmarking the MACE Foundation Model for Solid-State Ion Conductors — •Takeru Miyagawa, Yufeng Xu, Levon Satzger, Waldemar Kaiser, and David A. Egger
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12:30 |
MM 22.9 |
MACE-µ-α: A Foundation Model for Molecular Dipole Moments and Polarizabilities — •Nils Gönnheimer, Venkat Kapil, Karsten Reuter, and Johannes T. Margraf
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