Abstract
Perovskite solar cells (PSCs) have achieved great development since 2009 because of their unique optoelectronic properties. However, the critical challenges in perovskite photovoltaics still hinder their practical application. The performance of PSCs is governed by a number of indivisible factors during device fabrication, some of which are implicit and receive little attention. Conventional research often follows an iterative trial and error manner to optimize the PSCs, wherein the underlying mechanisms for major processing are not clear. Bayesian Optimization (BO) shows great potential for accelerating the development of processing chemistry for PSCs, which have received success in resolving the black-box problems in artificial intelligence (AI). In this Perspective, we briefly introduce the BO algorithm and review and discuss the applications of BO in the field of perovskite photovoltaics. Outlooks of the BO applications in processing chemistry of PSCs are proposed briefly.
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