Dresden 2020 – wissenschaftliches Programm

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CPP: Fachverband Chemische Physik und Polymerphysik

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 inferenceRyosuke 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
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DPG-Physik > DPG-Verhandlungen > 2020 > Dresden