Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.

中文翻译：

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为粗粒度分子模拟开发机器学习潜力：挑战和陷阱

粗粒度 (CG) 能够研究更大系统的分子特性，并且比原子分辨率下可达到的时间尺度更长。最近提出了机器学习技术来学习 CG 粒子相互作用，即开发 CG 力场。分子的图形表示和图形卷积神经网络架构的监督训练用于通过力匹配方案学习平均力的潜力。在这项工作中，作用在每个 CG 粒子上的力与其本地环境的学习表示相关，该表示以 SchNet 为名，通过连续滤波器卷积构建。我们探索了 SchNet 模型的应用以获得液态苯的 CG 势，研究模型架构和超参数对热力学的影响，