Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels
Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels
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DOI：
10.1088/2399-1984/ac3c8f
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发表时间：
2021-11
期刊：
影响因子：
2.1
通讯作者：
Bowen Zheng;Zeyu Zheng;Grace X. Gu
中科院分区：
文献类型：
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作者：
Bowen Zheng;Zeyu Zheng;Grace X. Gu
石墨烯气凝胶(GAS)是一类特殊的3D石墨烯组件，以其高强度、轻质和高孔隙率的特殊组合而闻名。然而，由于微观结构的随机性，气体的力学性质也具有高度的随机性，这是一个已经观察到但还没有得到充分解决的问题。在这项工作中，我们发展了高斯过程元模型，不仅可以预测气体的重要力学性质，还可以量化它们的不确定性。利用分子动力学模拟技术，将随机分布的石墨烯薄片和球形夹杂物组装成气体，然后在准静态单轴拉伸载荷下推导出力学性能。结果表明，在密度相同的情况下，材料的杨氏模数和极限抗拉强度等力学性能会发生很大的变化。将密度、杨氏模数和极限抗拉强度作为包裹体尺寸的函数，并以模拟遗传算法的结果作为训练数据，建立了能有效预测不可见气体性质的高斯过程元模型。此外，还建立了以预测为中心的统计有效的置信度区间。当数据采集需要昂贵的实验或计算时，这种元模型方法特别有用，GA模拟就是这种情况。目前的研究量化了气体的不确定的机械性质，这可能有助于对多种新型纳米材料的统计分析。
Graphene aerogels (GAs), a special class of 3D graphene assemblies, are well known for their exceptional combination of high strength, lightweightness, and high porosity. However, due to microstructural randomness, the mechanical properties of GAs are also highly stochastic, an issue that has been observed but insufficiently addressed. In this work, we develop Gaussian process metamodels to not only predict important mechanical properties of GAs but also quantify their uncertainties. Using the molecular dynamics simulation technique, GAs are assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to deduce mechanical properties. Results show that given the same density, mechanical properties such as the Young’s modulus and the ultimate tensile strength can vary substantially. Treating density, Young’s modulus, and ultimate tensile strength as functions of the inclusion size, and using the simulated GA results as training data, we build Gaussian process metamodels that can efficiently predict the properties of unseen GAs. In addition, statistically valid confidence intervals centered around the predictions are established. This metamodel approach is particularly beneficial when the data acquisition requires expensive experiments or computation, which is the case for GA simulations. The present research quantifies the uncertain mechanical properties of GAs, which may shed light on the statistical analysis of novel nanomaterials of a broad variety.