Unsupervised discovery of thin-film photovoltaic materials from unlabeled data

Abstract

Quaternary chalcogenide semiconductors (I2-II-IV-X4) are key materials for thin-film photovoltaics (PVs) to alleviate the energy crisis. Scaling up of PVs requires the discovery of I2-II-IV-X4 with good photoelectric properties; however, the structure search space is significantly large to explore exhaustively. The scarcity of available data impedes even many machine learning (ML) methods. Here, we employ the unsupervised learning (UL) method to discover I2-II-IV-X4 that alleviates the challenge of data scarcity. We screen all the I2-II-IV-X4 from the periodic table as the initial data and finally select eight candidates through UL. As predicted by ab initio calculations, they exhibit good optical conversion efficiency, strong optical responses, and good thermal stabilities at room temperatures. This typical case demonstrates the potential of UL in material discovery, which overcomes the limitation of data scarcity, and shortens the computational screening cycle of I2-II-IV-X4 by ~12.1 years, providing a research avenue for rapid material discovery.