Marc Stieffenhofer, Tristan Bereau, and Michael Wand, APL Materials (2021)
Chemical transferability of generative adversarial networks for backmapping
Switching between different levels of resolution is essential for multiscale modeling, but restoring details at higher resolution remains challenging. In our previous study, we have introduced deepBackmap, a deep neural-network-based approach to reverse-map equilibrated molecular structures for condensed-phase systems. Our method combines data-driven and physics-based aspects, leading to high-quality reconstructed structures. In this work, we expand the scope of our model and examine its chemical transferability. To this end, we train deepBackmap solely on homogeneous molecular liquids of small molecules and apply it to a more challenging polymer melt. We augment the generator’s objective with different force-field-based terms as a prior to regularize the results. The best performing physical prior depends on whether we train for a specific chemistry or transfer our model. Our local environment representation combined with the sequential reconstruction of fine-grained structures helps in reaching transferability of the learned correlations.