论文标题
聚合物接枝纳米颗粒之间平均力的深度学习潜力
Deep Learning Potential of Mean Force between Polymer Grafted Nanoparticles
论文作者
论文摘要
纳米颗粒表面上的聚合物链是控制其在聚合物基质中的自组装和分布的众所周知的途径。通过更改单个纳米颗粒表面上的嫁接模式来实现多种自组装结构。然而，确定确定其在聚合物基质中的组装和分布的一对移植的纳米颗粒之间平均力的有效潜力的准确估计是纳米科学的出色挑战。在这里，我们提出了一种新的深度学习方法，该方法从一组聚合物接枝的纳米颗粒的分子动力学轨迹中学习了一对移植的纳米颗粒之间的相互作用。随后，我们执行了基于平均力的分子模拟的深度学习潜力，该模拟预测了大量聚合物接枝的纳米颗粒的自组装到各种各向异性的上层结构中，包括渗透网络和双层均取决于3D中的纳米颗粒。平均强制预测的自组装上层建筑的深度学习潜力与聚合物移植的纳米颗粒的实际上层建筑一致。这个深度学习框架非常通用，可以加速自由空间或聚合物基质中聚合物接枝和不官能化的纳米颗粒的自组装和相位行为的表征和预测。
Grafting polymer chains on nanoparticles surfaces is a well-known route to control their self assembly and distribution in a polymer matrix. A wide variety of self assembled structures are achieved by changing the grafting patterns on an individual nanoparticle surface. However, accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. Here, we propose a new deep learning method that learns the interaction between a pair of grafted nanoparticles from the molecular dynamics trajectory of a cluster of polymer-grafted nanoparticles. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of polymer grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on nanoparticles concentration in 3D. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of polymer grafted nanoparticles. This deep learning framework is very generic and can accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.
