Machine Learning-Driven Advances in Perovskite Materials and Solar Cells

Area of Science:

• Optoelectronics
• Materials Science
• Renewable Energy

Background:

• Perovskite optoelectronics research is rapidly advancing, driven by renewable energy needs.
• Traditional experimental methods struggle with precise control of perovskite composition, microstructure, and degradation.
• Artificial Intelligence (AI) and Internet of Things (IoT) offer powerful solutions for material discovery and device optimization.

Purpose of the Study:

• To systematically review recent advancements in machine learning (ML) applications for perovskite solar cell (PSC) research.
• To cover ML implementations from molecular-scale screening to system performance evaluation.
• To identify challenges and future directions for ML-assisted perovskite development.

Main Methods:

• Review of literature on machine learning (ML) implementations in perovskite solar cell (PSC) research.
• Systematic summarization of ML applications in material screening, synthetic optimization, performance prediction, device design, and system evaluation.
• Analysis of obstacles and future research avenues.

Main Results:

• ML significantly enhances material discovery, synthetic condition design, and performance prediction for PSCs.
• ML facilitates molecular-scale material screening, synthetic parameter optimization, and device architecture design.
• Key challenges include operational stability, large-scale fabrication, and computational efficiency.

Conclusions:

• Machine learning (ML) is a transformative tool for advancing high-performance, manufacturable perovskite optoelectronic devices.
• Addressing current obstacles is crucial for realizing the full potential of ML in PSC research.
• Future research should focus on improving stability, scalability, and computational efficiency of ML-driven approaches.