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10:15 |
MM 18.1 |
Relation Between Element Specific Chemistry and Basis Set Size of Machine Learned Interatomic Potentials — •Haitham Gaafer, Jan Janssen, and Joerg Neugebauer
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10:30 |
MM 18.2 |
Physics-Based Generative Models: Enhanced Structure-Property Sampling in Inverse Materials Design — •Patricia König, Nicolas Bergmann, Piero Coronica, Chiara Panosetti, Hanna Türk, Karsten Reuter, and Christoph Scheurer
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10:45 |
MM 18.3 |
Active learning-based automated construction of Hamiltonian for structural phase transitions: a case study on BaTiO3 — •Mian Dai, Yixuan Zhang, Nuno Fortunato, Peng Chen, and Hongbin Zhang
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11:00 |
MM 18.4 |
Comparing linear and deep learning surrogate models of materials electronic structure — •Valdas Vitartas, Chen Qian, James Kermode, and Reinhard Maurer
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11:15 |
MM 18.5 |
MACE-H: Equivariant Hamiltonian prediction with many-body expansion message passing — •Chen Qian, Valdas Vitartas, James Kermode, and Reinhard J. Maurer
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11:30 |
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15 min. break
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11:45 |
MM 18.6 |
Inferring Structure-Property Relationships with Artificial Intelligence: A Lignin Case Study — •Matthias Stosiek and Patrick Rinke
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12:00 |
MM 18.7 |
Machine learning electrostatics: Open challenges from batteries to proteins — •Max Veit
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12:15 |
MM 18.8 |
Data driven prediction of relative stability of binary and ternary TCP phases. — •Mariano Forti, Ralf Drautz, and Thomas Hammerschmidt
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12:30 |
MM 18.9 |
Inverse Materials Design with Large Language Models — •Jan Janssen and Joerg Neugebauer
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12:45 |
MM 18.10 |
Workflow Utilities within the NOMAD Infrastructure: Lowering the Barrier to FAIR Data Management for Computational Materials Science — •J.F. Rudzinski, E. Boydas, N. Daelman, B. Mohr, J.M. Pizarro, T. Bereau, C. Draxl, L.M. Ghiringhelli, M. Girard, D. Usvyat, R. Valenti, and S. Botti
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13:00 |
MM 18.11 |
Freedom of design: towards in silico design of molecules with desired quantum-mechanical properties — •Leonardo Medrano Sandonas, Alessio Fallani, Julian Cremer, Alexandre Tkatchenko, and Gianaurelio Cuniberti
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