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MM: Fachverband Metall- und Materialphysik
MM 3: Data-driven Materials Science: Big Data and Worksflows
Montag, 17. März 2025, 10:15–13:00, H10
Machine Learning, Potential Development
10:15 | MM 3.1 | Benchmarking DFT Functionals at Finite Temperature with ASSYST and MLIPs — •Marvin Poul and Jörg Neugebauer | |
10:30 | MM 3.2 | Assessing the role of physical constraints in machine learning potentials — •Marcel F. Langer, Sergey N. Pozdnyakov, Filippo Bigi, and Michele Ceriotti | |
10:45 | MM 3.3 | Fast and flexible range-separated models for atomistic machine learning — •Philip Loche, Marcel F. Langer, and Michele Ceriotti | |
11:00 | MM 3.4 | Beyond Numerical Hessians: Applications for Higher Order Derivatives in Machine Learning Interatomic Potentials — •Nils Gönnheimer, Karsten Reuter, and Johannes T. Margraf | |
11:15 | MM 3.5 | Diversity-Driven Active Learning of Interatomic Potentials for Reaction Network Exploration — •Francesco Cannizzaro, King Chun Lai, Patricia Poths, Sebastian Matera, Vanessa J. Bukas, and Karsten Reuter | |
11:30 | 15 min. break | ||
11:45 | MM 3.6 | Accelerating Materials Exploration with Active Machine Learning: Integrating SISSO with FHI-aims — Yi Yao, Lucas Foppa, Akhil Sugathan Nair, Andrei Sobolev, •Konstantin Lion, Sebastian Kokott, and Matthias Scheffler | |
12:00 | MM 3.7 | Data-driven design of mechanically hard soft magnetic high-entropy alloys — •Mian Dai, Yixuan Zhang, Xiaoqing Li, Stephan Schönecker, Liuliu Han, Ruiwen Xie, Chen Shen, and Hongbin Zhang | |
12:15 | MM 3.8 | Autonomous optimization of coin-cell batteries and thin-film growth — •Edan Bainglass, Peter Kraus, Francisco Ramirez, Enea Svaluto-Ferro, Loris Ercole, Benjamin Kunz, Sebastiaan Huber, Nukorn Plainpan, Nikita Shepelin, Nicola Marzari, Corsin Battaglia, and Giovanni Pizzi | |
12:30 | MM 3.9 | Learning Disorder in Generative Materials Discovery - Bridging Prediction and Experiment — •Konstantin Jakob, Aron Walsh, Karsten Reuter, and Johannes T. Margraf | |
12:45 | MM 3.10 | Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Stable Oxides for Catalytic Applications — •Akhil S. Nair, Lucas Foppa, and Matthias Scheffler | |