基于图神经网络的粗粒化分子动力学
Coarse Graining Molecular Dynamics with Graph Neural Networks
摘要：
粗粒化使分子动力学的研究更大的系统和更长的时间尺度比原子分辨率。然而，必须制定一个粗粒度的模型，使我们从中得出的结论与我们从一个更精细的模型中得出的结论一致。它已被证明，力匹配方案定义了一个原子系统的变分极限的一致的粗粒度模型。Wang等人[ACS Cent.Sci. 5，755（2019）]证明了这种变分极限的存在使得能够使用监督机器学习框架来生成粗粒度力场，然后可以将其用于粗粒度空间中的模拟。然而，他们的框架需要人工输入分子特征，以便机器学习力场。在目前的贡献中，我们建立在Wang等人的基础上，并引入了一种用于粗粒度力场的机器学习的混合架构，该架构通过一个子网络来学习它们自己的特征，该子网络利用图神经网络架构上的连续滤波器卷积。我们证明，这个框架成功地再现小生物分子系统的热力学。由于学习的分子表示本质上是可转移的，因此本文提出的架构为开发可跨分子系统转移的机器学习的粗粒度力场奠定了基础。
英文摘要：
Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proven that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit. Wang et al. [ACS Cent. Sci. 5, 755 (2019)] demonstrated that the existence of such a variational limit enables the use of a supervised machine learning framework to generate a coarse-grained force field, which can then be used for simulation in the coarse-grained space. Their framework, however, requires the manual input of molecular features upon which to machine learn the force field. In the present contribution, we build upon the advance of Wang et al.and introduce a hybrid architecture for the machine learning of coarse-grained force fields that learns their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture. We demonstrate that this framework succeeds at reproducing the thermodynamics for small biomolecular systems. Since the learned molecular representations are inherently transferable, the architecture presented here sets the stage for the development of machine-learned, coarse-grained force fields that are transferable across molecular systems.
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