石墨烯
材料科学
极限抗拉强度
纳米晶材料
氧化物
机械强度
脆性
纳米技术
复合材料
冶金
作者
Yihua Xu,Qiao Shi,Ziyue Zhou,Ke Xu,Yan‐Wen Lin,Li Yang,Zhisen Zhang,Jianyang Wu
出处
期刊:2D materials
[IOP Publishing]
日期:2022-04-02
卷期号:9 (3): 035002-035002
被引量:13
标识
DOI:10.1088/2053-1583/ac635d
摘要
Abstract 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.
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