Recent progress in the data-driven discovery of novel photovoltaic materials

Abstract

The discovery of new photovoltaic materials can facilitate technological progress in clean energy and hence benefit overall societal development. Machine learning (ML) and deep learning (DL) technologies integrated with domain knowledge are revolutionizing the traditional trial-and-error research paradigm that is associated with high costs, inefficiency, and significant human effort. This review provides an overview of the recent progress in the data-driven discovery of novel photovoltaic materials for perovskite, dye-sensitized and organic solar cells. The integral workflow of the ML/DL training progress is briefly introduced, covering data preparation, feature engineering, model building and their applications. The cutting-edge challenges and issues in the ML/DL workflow are summarized specifically for photovoltaic materials. Real examples are emphasized to illustrate how to utilize ML/DL techniques in the discovery of novel photovoltaic materials. The prospects and future directions of the data-driven discovery of novel photovoltaic materials are also provided.

Keywords

Machine learning / materials design / deep learning / photovoltaic materials / data-driven / perovskite solar cells / organic solar cells / dye-sensitized solar cells