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.
Highlights
Computational modeling of soft-matter systems inherently requires the consideration of a wide range of time and length scales, where microscopic interactions can impact meso- to macroscopic changes.[1]
In this work we introduce DeepBackmap, a backmapping scheme based on deep convolutional neural networks
The model was applied to molecular dynamics (MD) configurations at T = 568 K, 453 K, and 313 K, each containing 78 samples that were not used during training
Summary
Computational modeling of soft-matter systems inherently requires the consideration of a wide range of time and length scales, where microscopic interactions can impact meso- to macroscopic changes.[1] Setting aside quantum mechanics, even molecular dynamics (MD) quickly reaches its limits when probing long relaxation times. The computational cost for the subsequent minimization and equilibration procedures can become significant for high-dimensional systems This is true for backmapping large numbers of coarse-grained configurations.[15] generating the initial atomistic structure often requires human intuition to avoid trapping in local minima. The protocol of Wassenaar et al needs to introduce geometric modifiers to correctly reproduce the distribution of torsion angles in phospholipids.[12]
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