The challenge of studying perovskite solar cells’ stability with machine learning

Frontiers in Energy Research

Energy Research

Noor Titan Putri Hartono¹  Antonio Abate¹  Hans Köbler¹  Eva Unger²  T. Jesper Jacobsson³  Carolin Ulbrich⁴  Mark Khenkin⁴  Rutger Schlatmann⁴  Paolo Graniero⁵ 

[1] Department Active Materials and Interfaces for Stable Perovskite Solar Cells, Helmholtz-Zentrum-Berlin, Berlin, Germany;Department of Solution-Processing of Hybrid Materials and Devices, Helmholtz-Zentrum-Berlin, Berlin, Germany;Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China;PVcomB, Helmholtz-Zentrum-Berlin, Berlin, Germany;PVcomB, Helmholtz-Zentrum-Berlin, Berlin, Germany;Department of Business Informatics, Freie Universität Berlin, Berlin, Germany;

关键词: perovskite solar cell;    stability;    machine learning;    figures of merit;    learning curves;    database;    feature importance analysis;    halide perovskite;   

DOI  :  10.3389/fenrg.2023.1118654

 received in 2022-12-07, accepted in 2023-03-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology.

【 授权许可】

Unknown   
Copyright © 2023 Graniero, Khenkin, Köbler, Hartono, Schlatmann, Abate, Unger, Jacobsson and Ulbrich.