MM 5: Topical Session: Physics-driven Artificial Intelligence for Materials I
Monday, March 9, 2026, 10:15–12:45, SCH/A251
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10:15 |
MM 5.1 |
Topical Talk:
Machine Learning for Materials Discovery: from Big Data to Predictive Insights — •Silvana Botti
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10:45 |
MM 5.2 |
Screening of high-entropy oxides as oxygen conductors for fuel cells — •Jesper R. Pedersen, Ciku Parida, Benjamin H. Sjølin, and Ivano E. Castelli
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11:00 |
MM 5.3 |
Interpretable Bayesian Optimization for Autonomous Materials Discovery — •Akhil S. Nair, Lucas Foppa, and Matthias Scheffler
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11:15 |
MM 5.4 |
Fantastic Polaronic Peaks and Where to Find Them: Learning Vibrational Spectra of a Disordered Energy Material — •Christoph Dähn, Yang Wang, Risov Das, Bettina V. Lotsch, Karsten Reuter, and Christian Carbogno
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11:30 |
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15 min. break
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11:45 |
MM 5.5 |
Topical Talk:
Leveraging data science technologies to enable AI-driven materials design — •Tilmann Hickel, Han Mai, Shankha Nag, Sarath Menon, Osamu Waseda, Liam Huber, Jan Janssen, and Jörg Neugebauer
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12:15 |
MM 5.6 |
Unveiling the Core of Materials Properties via SISSO and Sensitivity Analysis: Use-case Demonstration for Perovskites — •Lucas Foppa and Matthias Scheffler
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12:30 |
MM 5.7 |
Towards automated calculation of phase diagrams with machine learning interatomic potentials — •Sarath Menon and Ralf Drautz
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