Abstract: To promote the development of global carbon neutrality, perovskite solar cells (PSCs) have become a research hotspot in related fields. How to obtain PSCs with expected performance and explore the potential factors affecting device performance are the research priorities in related fields. Although some classical computational methods can facilitate material development, they typically require complex mathematical approximations and manual feature screening processes, which have certain subjectivity and one-sidedness, limiting the performance of the model. In order to alleviate the above challenges, this paper proposes a machine learning (ML) model based on neural networks. The model can assist both PSCs design and analysis of their potential mechanism, demonstrating enhanced and comprehensive auxiliary capabilities. To make the model have higher feasibility and fit the real experimental process more closely, this paper collects the corresponding real experimental data from numerous research papers to develop the model. Compared with other classical ML methods, the proposed model achieved better overall performance. Regarding analysis of underlying mechanism, the relevant laws explored by the model are consistent with the actual experiment results of existing articles. The model exhibits great potential to discover complex laws that are difficult for humans to discover directly. In addition, we also fabricated PSCs to verify the guidance ability of the model in this paper for real experiments. Eventually, the model achieved acceptable results. This work provides new insights into integrating ML methods and PSC design techniques, as well as bridging photovoltaic power generation technology and other fields.

Key words: Machine learning, Carbon neutrality, Photovoltaic, Auxiliary experiment design