论文标题
平衡的冷凝相分子结构的对抗反向映射
Adversarial Reverse Mapping of Equilibrated Condensed-Phase Molecular Structures
论文作者
论文摘要
分辨率之间的紧密联系的联系对于进一步扩大多尺度建模对复杂材料的影响至关重要。我们在这里将凝结的分子结构的产生作为粗粒结构的改进 - 背景图。传统方案从粗糙的粗到精细映射开始，并执行进一步的能量最小化和分子动力学模拟以平衡系统。在这项研究中，我们介绍了Deepbackmap：一种基于深神网络的方法，用于直接预测凝聚相系统的平衡分子结构。我们使用生成的对抗网络从训练数据中学习Boltzmann分布，并通过使用粗粒结构作为条件输入来实现反向映射。我们将方法应用于具有挑战性的凝聚相聚合系统。我们观察到，在熔体中训练的模型具有显着的转移性向晶相。仅使用有限的训练数据，我们建筑的数据驱动和基于物理方面的组合有助于达到温度转移性。
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 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 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. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data.
