Abstract
The rapid development of perovskite solar devices has led to a rising number of publications over the past decade. As a result, a project aiming to compile all published device data was initiated in 2022. However, with its method of manual data collection, one of the project's hurdles is encouraging the participation of the perovskite community to spend time and effort in inputting new device data. To ensure the project's sustainability, adequate participation is necessary but is challenging to achieve. In response to this, we propose the utilization of natural language processing algorithms to extract various attributes of perovskite solar devices from journal articles. When data collection is performed by programs instead of humans, the lack of community participation can be overcome. For each device, the identifying device information, intrinsic device data, extrinsic cell definition, and the details of the fabrication procedure were extracted. A total of 30 attributes from 3164 journal articles were compiled, with an average accuracy of 0.899. The dataset and source code are made publicly available.