Image-based machine learning for materials science

Materials research studies are dealing with a large number of images, which can now be facilitated via image-based machine learning techniques. In this article, we review recent progress of machine learning-driven image recognition and analysis for the materials and chemical domains. First, the image-based machine learning that facilitates the property prediction of chemicals or materials is discussed. Second, the analysis of nanoscale images including those from a scanning electron microscope and a transmission electron microscope is discussed, which is followed by the discussion about the identification of molecular structures via image recognition. Subsequently, the image-based machine learning works to identify and classify various practical materials such as metal, ceramics, and polymers are provided, and the image recognition for a range of real-scenario device applications such as solar cells is provided in detail. Finally, suggestions and future outlook for image based machine learning for classification and prediction tasks in the materials and chemical science are presented. This article highlights the importance of the integration of the image-based machine learning method into materials and chemical science and calls for a large-scale deployment of image-based machine learning methods for prediction and classification of images in materials and chemical science. Published under an exclusive license by AIP Publishing.