A Conceptual Model for Improving Perovskite Solar Cells Efficiency Using Machine Learning

Solar cells made from perovskites have experienced rapid development as examples of third-generation solar cells in recent years. The traditional trial-and-error method is inefficient, and the search space is incredibly large. This makes developing advanced perovskite materials, as well as high conversion efficiencies and stability of perovskite solar cells (PSCs), a challenging task. A growing number of data-driven machine learning (ML) applications are being developed in the materials science field, due to the availability of large databases and increased computing power. There are many advantages associated with the use of machine learning to predict the properties of potential perovskite materials, as well as provide additional knowledge on how these materials work to fast-track their progress. Thus, the purpose of this paper is to develop a conceptual model to improve the efficiency of a perovskite solar cell using machine learning techniques in order to improve its performance. This study relies on the application of design science as a method to conduct the research as part of the study. The developed model consists of six phases: Data collection and preprocessing, feature selection and engineering, model training and evaluation, performance assessment, optimization and fine-tuning, and deployment and application. As a result of this model, there is a great deal of promise in advancing the field of perovskite solar cells as well as providing a basis for developing more efficient and cost-effective solar energy technologies in the future.