Analysis on Hotspot Technologies and Cutting-edge Technologies of Organic Solar Cells Based on Patent Data

Abstract

The objective of this paper is to identify and forecast the technological development of organic solar cells by the analysis of patents. This paper researches the unsupervised machine learning methods and complex network theory. Based on the Python language, the K-means++ algorithm is used to cluster the data for discovering hotspot technologies, the theory of structure holes is used to identify potential cutting-edge technologies. The results indicate the hotspot technologies of organic solar cells are mainly involved in the five fields: device architecture and morphology control, electrode and electron transport layer design, active layer design, interface materials and fabrication, battery assembly and packaging process. And the potential cutting-edge technologies include: fabrication and morphology of active layer, device structure and interface engineering, transparent electrodes and substrates, donor and acceptor materials, and so on. The proposed analysis framework in this paper is applicable to different science and technology domains.