Dresden 2020 –
wissenschaftliches Programm
Die DPG-Frühjahrstagung in Dresden musste abgesagt werden! Lesen Sie mehr ...
CPP 32: Condensed-matter simulations augmented by advanced statistical methodologies (joint session DY/CPP)
Montag, 16. März 2020, 15:30–18:15, HÜL 186
 |
15:30 |
CPP 32.1 |
Funnel Hopping Monte Carlo: An efficient method to overcome broken ergodicity — •Jonas Alexander Finkler and Stefan Goedecker
|
|
|
|
15:45 |
CPP 32.2 |
Second-principles investigation of the electrocaloric properties of PbTiO3 — •Monica Graf and Jorge Iñiguez
|
|
|
|
16:00 |
CPP 32.3 |
Exploring Chemical Reaction Space with Machine Learning — •Sina Stocker, Gábor Csányi, Karsten Reuter, and Johannes T. Margraf
|
|
|
|
16:15 |
CPP 32.4 |
Kernel-based machine learning for efficient molecular liquid simulations — •Christoph Scherer, René Scheid, Tristan Bereau, and Denis Andrienko
|
|
|
|
16:30 |
CPP 32.5 |
Anharmonic phonons sampled from large scale molecular dynamics based on on-the-fly machine- learning force fields — •Jonathan Lahnsteiner and Menno Bokdam
|
|
|
| |
16:45 |
|
15 min. break.
|
|
|
|
17:00 |
CPP 32.6 |
Edgy and Parallel – Efficient Equilibration of Anisotropic Hard Particulate Systems — •Marco Klement and Michael Engel
|
|
|
|
17:15 |
CPP 32.7 |
Machine-learning force fields trained on-the-fly with bayesian inference — Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, and •Menno Bokdam
|
|
|
|
17:30 |
CPP 32.8 |
Learning effective collective variables for biasing via t-distributed stochastic neighbor embedding — •Omar Valsson and Jakub Rydzewski
|
|
|
|
17:45 |
CPP 32.9 |
Variational autoencoders as a tool to learn collective variables from simulation snapshots — •Miriam Klopotek and Martin Oettel
|
|
|
|
18:00 |
CPP 32.10 |
Adversarial Reverse Mapping of Equilibrated Condensed-Phase Molecular Structures — •Marc Stieffenhofer, Michael Wand, and Tristan Bereau
|
|
|
