Analysis of High Efficient Perovskite Solar Cells Using Machine Learning

Affordable manufacturing along with high efficiency perovskite solar cell in photovoltaic technology has everyone's attention. Perovskite, which is in the lead role in solar cells, is full of characteristics such as high absorption coefficient, low exciton binding energy, charge carrier capable of having better mobility as well as more diffusion length and availability in suitable energy band. The application of machine learning technology is proving to be a boon to ensure optimum implementation with different properties in photovoltaic device, design, simple construction process and low-cost price. Machine learning is a branch of artificial intelligence which includes large data aggregation, precise structure property installation, demonstration and final model after model validation. The most of the source of database is the simulation and experimental results, calculations and related literature surveys which have a comprehensive compilation of the performance of hybrid perovskite device, collection of structures and properties of elements. Structure-property relationship installation comes under feature engineering which establishes a clear relationship between structure and the properties. In other demonstration process, proper algorithms are selected, data is generated and tested as well as pure estimated values are taken. This article contains a detailed discussion on the involvement of machine learning technology to build high-performance Perovskite solar cells. Proper selection as well as designing of active perovskite absorbent layer by machine learning successfully establishes results by including other parts such as non-toxic (lead free) and stability. Mature machine learning technology becomes a very essential method in determining the solvent combination of hybrid perovskite and in estimating design of the entire solar cell to ensure optimum implementation in the sector of perovskite solar technology. Finally, a phased concept has been briefly discussed to meet the challenges of machine learning and potential future compatibilities related to the prevalence.