Adversarial reverse mapping of equilibrated condensed-phase molecular structures. Issue 4 (15th October 2020)

• Record Type: Journal Article
• Authors: Stieffenhofer, Marc; Wand, Michael; Bereau, Tristan
• Is Part Of: Machine learning: science and technology. Volume 1:Issue 4(2020)
• Publication Date: 2020-10-15
• Subjects: backmapping -- multiscale modeling -- generative adversarial networks -- molecular dynamics -- condensed phase; 006.31
• DOI: 10.1088/2632-2153/abb6d4
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement—backmapping—of a coarse-grained (CG) structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural 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 CG 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. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data.