Chen An, Wang Zhilong, Gao Jing, Han Yanqiang, Cai Junfei, Ye Simin, Li Jinjin
Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China.
ACS Nano. 2023 Jul 25;17(14):13348-13357. doi: 10.1021/acsnano.3c01442. Epub 2023 Jul 5.
The exceptional properties of two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs) have led to a rapid increase in the number of low-dimensional materials for optoelectronic engineering and solar energy conversion. The flexibility and controllability of 2D HOIPs create a vast structural space, which presents an urgent issue to effectively explore 2D HOIPs with better performance for practical applications. However, the traditional RP-DJ classification method falls short in describing the influence of structure on the electronic properties of 2D HOIPs. To overcome this limitation, we employed inorganic structure factors (SF) as a classification descriptor, which considers the influence of inorganic layer distortion of 2D HOIPs. And we investigated the relationship between SF, other physicochemical features, and band gaps of 2D HOIPs. By using this structural descriptor as a feature for a machine learning model, a database of 304920 2D HOIPs and their structural and electronic properties was generated. A large number of previously neglected 2D HOIPs were discovered. With the establishment of this database, experimental data and machine learning methods were combined to develop a 2D HOIPs exploration platform. This platform integrates searching, download, analysis, and online prediction, providing a useful tool for the further discovery of 2D HOIPs.
二维有机-无机杂化铅卤化物钙钛矿（2D HOIPs）的优异特性，使得用于光电子工程和太阳能转换的低维材料数量迅速增加。2D HOIPs的灵活性和可控性创造了广阔的结构空间，这就提出了一个紧迫的问题，即如何有效地探索具有更好性能的2D HOIPs以用于实际应用。然而，传统的RP-DJ分类方法在描述结构对2D HOIPs电子性质的影响方面存在不足。为克服这一局限性，我们采用无机结构因子（SF）作为分类描述符，该因子考虑了2D HOIPs无机层畸变的影响。并且我们研究了SF、其他物理化学特征与2D HOIPs带隙之间的关系。通过将此结构描述符用作机器学习模型的一个特征，生成了一个包含304920种2D HOIPs及其结构和电子性质的数据库。发现了大量之前被忽视的2D HOIPs。随着这个数据库的建立，将实验数据和机器学习方法相结合，开发了一个2D HOIPs探索平台。该平台集成了搜索、下载、分析和在线预测功能，为进一步发现2D HOIPs提供了一个有用的工具。
