Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session C22: Emerging Trends in MD Simulations and Machine Learning II
3:00 PM–6:00 PM,
Monday, March 15, 2021
Sponsoring
Units:
DCOMP GDS DSOFT DPOLY
Chair: Priya Vashishta, Univ of Southern California
Abstract: C22.00001 : Backmapping of Equilibrated Condensed-Phase Molecular Structures with Generative Adversarial Networks*
3:00 PM–3:12 PM
Live
Presenter:
Marc Stieffenhofer
(Max Planck Institute for Polymer Research)
Authors:
Marc Stieffenhofer
(Max Planck Institute for Polymer Research)
Michael Wand
(Institute of Computer Science, Johannes Gutenberg University)
Tristan Bereau
(Van 't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam)
network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the coarse-grained structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase.
*This work was supported in part by the TRR 146 Collaborative Research Center of the Deutsche Forschungsgemeinschaft as well as the Max Planck Graduate Center. Tristan Bereau acknowledges financial support by the Emmy Noether program of the Deutsche Forschungsgemeinschaft (DFG).
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