O 83: Frontiers of Electronic Structure Theory: Focus on Artificial Intelligence Applied to Real Materials 4
Freitag, 9. September 2022, 10:30–13:00, S054
| |
10:30 |
O 83.1 |
The contribution has been moved to MM 20.7.
|
|
|
 |
10:45 |
O 83.2 |
Stacking the odds: Distribution-biased generative deep learning for molecular design — •Joe Gilkes, Julia Westermayr, Rhyan Barrett, and Reinhard J. Maurer
|
|
|
|
11:00 |
O 83.3 |
Machine learning TCP phases with domain knowledge of the interatomic bond — •Mariano Forti, Alesya Burakovskaya, Ralf Drautz, and Thomas Hammerschmidt
|
|
|
|
11:15 |
O 83.4 |
Ab initio random structure search of organic molecules at substrates — •Dmitrii Maksimov and Mariana Rossi
|
|
|
|
11:30 |
O 83.5 |
Active learning and element-embedding approach in neural networks for infinite-layer versus perovskite oxides — Armin Sahinovic and •Benjamin Geisler
|
|
|
|
11:45 |
O 83.6 |
Indirect learning interatomic potential models for accelerated materials simulations — •Joe D. Morrow and Volker L. Deringer
|
|
|
|
12:00 |
O 83.7 |
Predicting hot electrons free energies from ground-state data — •Chiheb Ben Mahmoud, Federico Grasselli, and Michele Ceriotti
|
|
|
| |
12:15 |
O 83.8 |
The contribution has been withdrawn.
|
|
|
|
12:30 |
O 83.9 |
MD-based Raman Spectra using Machine Learning — •Manuel Grumet, Karin S. Thalmann, Tomáš Bučko, and David A. Egger
|
|
|
|
12:45 |
O 83.10 |
Thermal Transport via Green-Kubo Method and Message-Passing Neural-Network Potentials — Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, and •Matthias Rupp
|
|
|
