Dresden 2020 –
wissenschaftliches Programm
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CPP 104: Topical Session: Data Driven Materials Science - Machine Learning Applications (joint session MM/CPP)
Donnerstag, 19. März 2020, 17:30–19:00, BAR 205
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17:30 |
CPP 104.1 |
How polymorphism of adsorbate molecules determines the physical properties of metal/organic interfaces: a large scale study — •Johannes J. Cartus, Andreas Jeindl, Lukas Hörmann, and Oliver T. Hofmann
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17:45 |
CPP 104.2 |
Investigation of short-range order in multicomponent alloys with the use of machine-learning interatomic potentials — •Tatiana Kostiuchenko, Alexander Shapeev, Fritz Körmann, and Andrey Ruban
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18:00 |
CPP 104.3 |
An equation for membrane permeability: Insight from compressed sensing — •Arghya Dutta and Tristan Bereau
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18:15 |
CPP 104.4 |
Transferable Gaussian Process Regression for prediction of molecular crystals harmonic free energy. — •Marcin Krynski and Mariana Rossi
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18:30 |
CPP 104.5 |
Bayesian modeling for potential energy surface minimization — •Estefania Garijo del Rio, Sami Juhani Kaapa, and Karsten Wedel Jacobsen
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18:45 |
CPP 104.6 |
A computational route between band mapping and band structure — R. Patrick Xian, •Vincent Stimper, Marios Zacharias, Shuo Dong, Maciej Dendzik, Samuel Beaulieu, Matthias Scheffler, Bernhard Schölkopf, Martin Wolf, Laurenz Rettig, Christian Carbogno, Stefan Bauer, and Ralph Ernstorfer
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