The mechanical properties of graphene oxides (GOs) are of great importance for their practical applications. Herein, extensive first-principles-based ReaxFF molecular dynamics (MD) simulations predict the wrinkling morphology and mechanical properties of nanocrystalline GOs (NCGOs), with intricate effects of grain size, oxidation, hydroxylation, epoxidation, grain boundary (GB) hydroxylation, GB epoxidation, GB oxidation being considered. NCGOs show brittle failures initiating at GBs, obeying the weakest link principle. By training the MD data, four machine learning models are developed with capability in estimating the tensile strength of NCGOs, with sorting as eXtreme Gradient Boosting (XGboost) > multilayer perceptron > gradient boosting decision tree > random forest. In the XGboot model, it is revealed that the strength of NCGOs is greatly dictated by oxidation and grain size, and the hydroxyl group plays more critical role in the strength of NCGOs than the epoxy group. These results uncover the pivotal roles of structural signatures in the mechanical strength of NCGOs, and provide critical guidance for mechanical designs of chemically-functionalized nanostructures.

中文翻译：

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机器学习有助于深入了解纳米晶氧化石墨烯的机械强度

氧化石墨烯 (GO) 的机械性能对其实际应用非常重要。在此，广泛的基于第一性原理的 ReaxFF 分子动力学 (MD) 模拟预测纳米晶 GO (NCGO) 的起皱形态和机械性能，具有晶粒尺寸、氧化、羟基化、环氧化、晶界 (GB) 羟基化、GB环氧化，正在考虑GB氧化。NCGO 显示在 GB 处开始的脆性故障，遵循最薄弱环节原则。通过训练MD数据，开发了四种具有估计NCGO抗拉强度能力的机器学习模型，排序为eXtreme Gradient Boosting（XGboost）>多层感知器>梯度提升决策树>随机森林。在 XGboot 模型中，结果表明，NCGO 的强度在很大程度上受氧化和晶粒尺寸的影响，羟基对 NCGO 强度的影响比环氧基更重要。这些结果揭示了结构特征在 NCGO 机械强度中的关键作用，并为化学功能化纳米结构的机械设计提供了关键指导。