Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Phys Rev E. 2017 Nov;96(5-1):052111. doi: 10.1103/PhysRevE.96.052111. Epub 2017 Nov 9.
We apply recent advances in machine learning and computer vision to a central problem in materials informatics: the statistical representation of microstructural images. We use activations in a pretrained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone.
微观结构图像的统计表示。我们使用预训练的卷积神经网络中的激活函数为一组合成微观结构图像提供高维特征描述。接下来，我们使用流形学习来获得此统计特征的低维嵌入。我们表明，低维嵌入提取了用于生成图像的参数。根据各种指标，卷积神经网络方法生成的嵌入明显优于仅从两点相关得出的类似方法。
