Automatically Predicting Material Properties with Microscopic Images: Polymer Miscibility as an Example.

Abstract

Many material properties are manifested in the morphological appearance and characterized using microscopic images, such as scanning electron microscopy (SEM). Polymer miscibility is a key physical quantity of polymer materials and is commonly and intuitively judged using SEM images. However, human observation and judgment of the images is time-consuming, labor-intensive, and hard to be quantified. Computer image recognition with machine learning methods can make up for the defects of artificial judging, giving accurate and quantitative judgment. We achieve automatic miscibility recognition utilizing a convolutional neural network and transfer learning methods, 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.