Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility
This work addresses the time-consuming and labor-intensive task of material property assessment for researchers in materials science, offering an incremental improvement through automation.
The paper tackles the problem of manually judging polymer miscibility from SEM images by developing an automated recognition system using convolutional neural networks and transfer learning, achieving up to 94% accuracy and introducing a quantitative criterion for miscibility.
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.