Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an 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 proved 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 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 learn 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.

中文翻译：

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使用图神经网络进行粗粒度分子动力学

与原子分辨率相比，粗粒度可以研究更大系统的分子动力学和更长的时间尺度。但是，必须制​​定粗粒度模型，以便我们从中得出的结论与我们从更详细的模型中得出的结论一致。已经证明，力匹配方案为处于变分极限的原子系统定义了热力学一致的粗粒度模型。王等人。[ACS 分。科学。5, 755 (2019)] 证明了这种变分限制的存在使得可以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，他们的框架需要手动输入分子特征来机器学习力场。在目前的贡献中，我们建立在 Wang等人的进步之上。并引入了一种混合架构，用于粗粒度力场的机器学习，通过利用图神经网络架构上的连续滤波器卷积的子网络来学习自己的特征。我们证明该框架成功地再现了小生物分子系统的热力学。由于学习到的分子表示本质上是可转移的，这里介绍的架构为机器学习的、可跨分子系统转移的粗粒度力场的发展奠定了基础。