Perovskite solar cells towards industrialization: overcoming challenges with data-driven strategies

Area of Science:

• Materials Science
• Renewable Energy Engineering
• Computational Science

Background:

• Perovskite solar cells (PSCs) show high power conversion efficiency (PCE) and low-cost potential.
• Commercialization is limited by operational stability and scalable fabrication challenges.
• Traditional R&D methods are insufficient for complex PSC issues.

Purpose of the Study:

• To synthesize the PSC industrial landscape and identify key bottlenecks.
• To establish a framework mapping machine learning (ML) techniques to PSC industrialization challenges.
• To outline a roadmap for data-driven PSC development.

Main Methods:

• Review and synthesis of the PSC industrial landscape.
• Analysis of stability, scalability, and cost bottlenecks.
• Mapping ML techniques (e.g., virtual screening, process optimization) to solutions.

Main Results:

• ML decodes non-linear structure-process-property relationships.
• ML accelerates the discovery of stable materials and optimizes manufacturing.
• ML predicts device performance and lifetime, addressing industrialization challenges.

Conclusions:

• ML offers a powerful paradigm to overcome PSC commercialization barriers.
• Standardized data, explainable AI, and automation are crucial for a data-driven future.
• A strategic roadmap is proposed to realize the full potential of data-driven PSC development.