Machine learning-accelerated development of perovskite optoelectronics toward efficient energy harvesting and conversion

Abstract

For next-generation optoelectronic devices with efficient energy harvesting and conversion, designing advanced perovskite materials with exceptional optoelectrical properties is highly critical. However, the conventional trial-and-error approaches usually lead to long research periods, high costs, and low efficiency, which hinder the efficient development of optoelectronic devices for broad applications. The machine learning (ML) technique emerges as a powerful tool for materials designs, which supplies promising solutions to break the current bottlenecks in the developments of perovskite optoelectronics. Herein, the fundamental workflow of ML to interpret the working mechanisms step by step from a general perspective is first demonstrated. Then, the significant contributions of ML in designs and explorations of perovskite optoelectronics regarding novel materials discovery, the underlying mechanisms interpretation, and large-scale information process strategy are illustrated. Based on current research progress, the potential of ML techniques in cross-disciplinary directions to achieve the boost of material designs and optimizations toward perovskite materials is pointed out. In the end, the current advances of ML in perovskite optoelectronics are summarized and the future development directions are shown. This perspective supplies important insights into the developments of perovskite materials for the next generation of efficient and stable optoelectronic devices.