  Many material properties are manifested in the morphological appearance and
characterized with microscopic image, such as scanning electron microscopy
(SEM). Polymer miscibility is a key physical quantity of polymer material and
commonly and intuitively judged by SEM images. However, human observation and
judgement for the images is time-consuming, labor-intensive and hard to be
quantified. Computer image recognition with machine learning method can make up
the defects of artificial judging, giving accurate and quantitative judgement.
We achieve automatic miscibility recognition utilizing convolution neural
network and transfer learning method, and the model obtains up to 94% accuracy.
We also put forward a quantitative criterion for polymer miscibility with this
model. The proposed method can be widely applied to the quantitative
characterization of the microstructure and properties of various materials.
