Piling graphene sheets into a bulk form is essential for achieving massive applications of graphene in flexible structures and devices, and the arbitrary shape, random distributions, and adjacent overlaps of graphene sheets are yet challenging the prediction of its fundamental properties that are strongly coupled by mechanical strength and thermal or electronic transport. Here, we present a deep neural network (DNN)-based machine learning (ML) approach that enables the prediction of thermal conductivity of piled graphene structures with a broad range of geometric configurations and dimensions in response to external mechanical loading. A physics-informed pixel value matrix is developed to capture the key geometric features of piled graphene structures and is incorporated into the DNN to train the ML model with the only training data ratio of 12.5% but the prediction accuracy of 94%. The ML model is further extended with the transferred knowledge from primitive training data sets to predict the thermal transport of piled graphene in a custom data set. Extensive demonstrations in search of piled graphene structures with desirable thermal conductivity and its response to mechanical loading are presented and illustrate the capability and accuracy of the DNN-ML model for establishing a mechanically adaptive structure: responsive thermal property paradigm in piled graphene. This work lays a foundation for quantitatively evaluating thermal conductivity of piled graphene in response to mechanical loadings through an ML model and also offers a rational route for exploring mechanically tunable thermal properties of nanomaterial-based bulk forms, potentially useful in the design of flexible thermal structures and devices with controllable thermal management performance.

中文翻译：

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机械拉伸石墨烯薄片中热传输的可转移、深度学习驱动的快速预测和设计

将石墨烯片堆积成块状对于在柔性结构和器件中实现石墨烯的大规模应用至关重要，而石墨烯片的任意形状、随机分布和相邻重叠仍对其基本性质的预测提出挑战，这些基本性质与机械强度密切相关。强度和热或电子传输。在这里，我们提出了一种基于深度神经网络 (DNN) 的机器学习 (ML) 方法，该方法能够预测具有广泛几何配置和尺寸的堆积石墨烯结构的热导率，以响应外部机械载荷。开发了一个基于物理的像素值矩阵来捕获堆积石墨烯结构的关键几何特征，并将其合并到 DNN 中以训练 ML 模型，其训练数据比率仅为 12。5% 但预测准确率为 94%。ML 模型进一步扩展了从原始训练数据集中转移的知识，以预测自定义数据集中堆积石墨烯的热传输。展示了寻找具有理想热导率的堆积石墨烯结构及其对机械载荷的响应的广泛演示，并说明了 DNN-ML 模型用于建立机械自适应结构的能力和准确性：堆积石墨烯中的响应热特性范例。这项工作为通过 ML 模型定量评估堆积石墨烯响应机械载荷的热导率奠定了基础，也为探索基于纳米材料的块体形式的机械可调热性能提供了合理的途径，