MM 13: Data-driven Materials Science: Big Data and Workflows I
Tuesday, March 10, 2026, 10:15–12:45, SCH/A251
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10:15 |
MM 13.1 |
Surface reconstruction via automated LEED analysis based on Bayesian optimization — •Xiankang Tang and Hongbin Zhang
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10:30 |
MM 13.2 |
Structural relaxations for nonstoichiometric alloys without forces — •Luca Numrich and Hongbin Zhang
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10:45 |
MM 13.3 |
A Python-based workflow for phase identification and mapping via Raman spectroscopy — •Felix Drechsler, Mahnaz Mehdizadehlima, Cameliu Himcinschi, David Rafaja, and Jens Kortus
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11:00 |
MM 13.4 |
Automated Prediction of Phase Stability with ab-initio Accuracy — •Prabhath Chilakalapudi, Marvin Poul, Jan Janssen, and Jörg Neugebauer
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11:15 |
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15 min. break
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11:30 |
MM 13.5 |
Towards Disorder-Aware Materials Discovery - Recognizing and Modeling Crystallographic Disorder — •Konstantin S. Jakob, Aron Walsh, Karsten Reuter, and Johannes T. Margraf
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11:45 |
MM 13.6 |
Efficient Exploration of the Unknown: Distance-Based Active Learning with SISSO Descriptors and Mendeleev Similarities for Materials Discovery — •Sreejani Karmakar, Akhil S. Nair, Lucas Foppa, and Matthias Scheffler
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12:00 |
MM 13.7 |
Where Are Large Language Models Actually Useful for Materials Design? — •Hedda Oschinski, Maximilian L. Ach, David Greten, Konstantin S. Jakob, Christian Carbogno, and Karsten Reuter
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12:15 |
MM 13.8 |
Predictive and interpretable machine learning models for thermodynamics tuning of metal hydrides for hydrogen storage — •Sinan S. Faouri, Kai Sellschopp, Claudio Pistidda, and Paul Jerabek
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12:30 |
MM 13.9 |
Score-based diffusion models for accurate crystal structure inpainting and reconstruction of hydrogen positions — •Timo Reents, Arianna Cantarella, Marnik Bercx, Pietro Bonfà, and Giovanni Pizzi
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