Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
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DOI：
10.1038/s41524-021-00603-8
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发表时间：
2021-08-19
影响因子：
9.7
通讯作者：
Maschmann, Matthew R.
中科院分区：
文献类型：
--
作者：
Hajilounezhad, Taher;Bao, Rina;Maschmann, Matthew R.
理解和控制垂直定向碳纳米管（CNT）森林的自组装对于实现其在无数应用中的潜力至关重要。对控制过程-结构-性质的机制知之甚少，并且过程参数空间太大，无法通过实验进行详尽的探索。我们通过使用基于物理的模拟作为高通量虚拟实验室和基于图像的机器学习将CNT森林合成属性与其机械性能相关联来克服这些限制。使用CNTNet，我们的基于图像的深度学习分类器模块使用合成图像进行训练，CNT直径，密度和人口增长率类的组合被标记为准确率> 91%。CNTNet回归模块预测CNT森林刚度和屈曲载荷特性的均方根误差低于基于CNT物理参数的回归预测器。这些结果表明，仅使用模拟图像训练的基于图像的机器学习可以区分细微的CNT森林形态特征，以高精度预测物理材料特性。CNTNet为将扫描电子显微镜图像用于高通量材料发现铺平了道路。
Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process-structure-property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.