Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study. Coarse-graining (CG) allows to establish a link between different system resolutions and provides the backbone for the development of robust multiscale simulations and analyses. The CG mapping process is typically system- and application-specific, and it relies on chemical intuition. In this work, we explored the application of a Machine Learning strategy, based on Variational Autoencoders, for the development of suitable mapping schemes from the atomistic to the coarse-grained space of molecules with increasing chemical complexity. An extensive evaluation of the effect of the model hyperparameters on the training process and on the final output was performed, and an existing method was extended with the definition of different loss functions and the implementation of a selection criterion that ensures physical consistency of the output. The relationship between the input feature choice and the reconstruction accuracy was analyzed, supporting the need to introduce rotational invariance into the system. Strengths and limitations of the approach, both in the mapping and in the backmapping steps, are highlighted and critically discussed.

中文翻译：

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基于机器学习的有机分子粗粒度映射方案的研究

由于大分子系统中存在广泛的时间尺度，分层多尺度策略对于它们的计算研究是必要的。粗粒度 (CG) 允许在不同系统分辨率之间建立联系，并为开发稳健的多尺度模拟和分析提供基础。CG 映射过程通常是特定于系统和应用程序的，它依赖于化学直觉。在这项工作中，我们探索了基于变分自动编码器的机器学习策略的应用，以开发从原子到化学复杂性增加的分子的粗粒度空间的合适映射方案。对模型超参数对训练过程和最终输出的影响进行了广泛的评估，并且通过定义不同的损失函数和实施确保输出物理一致性的选择标准来扩展现有方法。分析了输入特征选择与重建精度之间的关系，支持将旋转不变性引入系统的需要。该方法的优势和局限性，无论是在映射还是在反向映射步骤中，都被强调和批判性地讨论。