The Spring of Processing Chemistry in Perovskite Solar Cells-Bayesian Optimization

Area of Science:

• Materials Science
• Renewable Energy
• Photovoltaics

Background:

• Perovskite solar cells (PSCs) exhibit remarkable optoelectronic properties, driving significant advancements since 2009.
• Critical challenges in PSC fabrication and processing chemistry impede widespread practical application.
• Current optimization strategies often rely on iterative trial-and-error, lacking mechanistic understanding.

Purpose of the Study:

• To introduce the Bayesian Optimization (BO) algorithm.
• To review and discuss the application of BO in perovskite photovoltaics.
• To propose future outlooks for BO in PSC processing chemistry.

Main Methods:

• Introduction to the principles of the Bayesian Optimization algorithm.
• Review of existing literature on BO applications in perovskite solar cell research.
• Discussion of implicit factors influencing PSC performance during fabrication.

Main Results:

• Bayesian Optimization demonstrates potential for accelerating the optimization of PSC processing.
• BO can effectively address 'black-box' optimization problems common in materials science.
• Successful applications of BO in resolving complex processing challenges within perovskite photovoltaics.

Conclusions:

• Bayesian Optimization offers a powerful, data-driven approach to overcome limitations in conventional PSC development.
• Further exploration of BO in PSC processing chemistry is crucial for enhancing device performance and manufacturability.
• BO can provide deeper mechanistic insights into PSC fabrication, moving beyond empirical optimization.