K. Burns, Kayvon Tadj, Tarun Allaparti, Liliana Arias, Nan Li, A. Aitkaliyeva, Amit Misra, M. Scott, Khalid Hattar
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引用次数: 0
摘要
利用卷积神经网络(CNN)对时间序列数据建模,需要建立一个分批学习的模型,而不是按顺序进行训练。将卷积神经网络与原位或操作技术相结合,可以准确地分割动态反应和质量传输现象,从而了解材料在使用条件下的行为。在本文中,原位离子照射透射电子显微镜(TEM)图像被用作 CNN 的输入,以评估缺陷生成率、缺陷群密度和缺陷饱和度。然后,我们使用输出分割图与传统 TEM 显微照片进行关联,以评估该模型详细描述纳米级相互作用的能力。接下来,我们讨论了预处理和超参数对模型变异性的影响、扩展到其他数据集时的准确性以及正则化在控制模型变异性时的作用。最终,我们消除了推断物理指标时的人为偏差,加快了分析时间,解耦了以 100 毫秒间隔发生的反应,并部署了既准确又可移植到类似实验的模型。
Deep learning-enabled probing of irradiation-induced defects in time-series micrographs
Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.