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15:45 |
MM 31.1 |
Neural networks trained on synthetically generated crystals can classify space groups of ICSD powder X-ray diffractograms — •Henrik Schopmans, Patrick Reiser, and Pascal Friederich
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16:00 |
MM 31.2 |
Critical Assessment of Uncertainty Estimates of Machine- Learning Potentials — •Shuaihua Lu, Luca M. Ghiringhelli, Christian Carbogno, and Matthias Scheffler
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16:15 |
MM 31.3 |
Learning to Spell Materials - Coordinate-free Discovery with Natural Language Processing — •Konstantin Jakob, Karsten Reuter, and Johannes T. Margraf
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16:30 |
MM 31.4 |
Exploring materials dataspaces by combining supervised and unsupervised machine learning — •Andreas Leitherer, Angelo Ziletti, Christian H. Liebscher, Timofey Frolov, and Luca M. Ghiringhelli
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16:45 |
MM 31.5 |
The contribution has been withdrawn.
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17:00 |
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15 min. break
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17:15 |
MM 31.6 |
Accelerating the Search for High-Performance, Novel Materials with Active Learning — Thomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli, and •Christian Carbogno
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17:30 |
MM 31.7 |
Machine learning discovery of new materials — •Jonathan Schmidt, Hai-Chen Wang, Noah Hoffman, Tiago Cerqueira, Pedro Borlido, Pedro Carrico, Love Pettersson, Claudio Verdozzi, Silvana Botti, and Miguel Marques
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17:45 |
MM 31.8 |
Data-driven magneto-elastic interatomic potentials for discovering novel phases of transition metal alloys — •Mani Lokamani, Kushal Ramakrishna, Julian Tranchida, Svetoslav Nikolov, Hossein Tahmasbi, Michael Wood, and Attila Cangi
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18:00 |
MM 31.9 |
FAIR Modelling Recipes for High-Throughput Screening of Metal Hydrides — •Kai Sellschopp, Philipp Zschumme, Michael Selzer, Claudio Pistidda, and Paul Jerabek
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18:15 |
MM 31.10 |
Take Two: Δ-Machine Learning for Molecular Co-Crystals — •Simon Wengert, Gábor Csányi, Karsten Reuter, and Johannes Theo Margraf
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