Development of High-Efficiency Perovskite Solar Cells and Their Integration with Machine Learning.

Area of Science:

• Materials Science
• Renewable Energy
• Photovoltaics

Background:

• Perovskite solar cells are a promising third-generation photovoltaic technology.
• They exhibit high light absorption, tunable bandgaps, and high power conversion efficiency.
• Significant progress has been made since their invention, but technical bottlenecks remain.

Purpose of the Study:

• To review technological breakthroughs and the current status of perovskite solar cells.
• To analyze the characteristics and limitations of lead-based perovskite systems.
• To identify obstacles to commercialization and explore future development directions.

Main Methods:

• Systematic review of perovskite solar cell development and efficiency improvements.
• Analysis of material characteristics, limitations, and commercialization challenges.
• Exploration of machine learning applications in perovskite material discovery and optimization.

Main Results:

• Technological advancements have continuously improved perovskite solar cell efficiency.
• Operational instability and large-scale manufacturing are critical commercialization hurdles.
• Machine learning shows potential for accelerating material discovery and process optimization.

Conclusions:

• Perovskite solar cells hold substantial potential for future energy applications.
• Addressing stability and manufacturing issues is crucial for widespread adoption.
• Machine learning can facilitate a closed-loop research framework for advancing perovskite technology.