Halide perovskite materials serve as excellent candidates for solar cell and optoelectronic devices. Recently, the design of the halide perovskite materials is greatly facilitated by machine learning techniques, which effectively identify suitable halide perovskite candidates and unveil hidden relationships by algorithms that mimic the human cognitive functions. In this manuscript, we review recent progresses on the machine learning studies of the halide perovskite materials, including the prediction and understanding of lead-free and stable halide perovskite materials. The structural descriptors to describe the property and performance of the halide perovskite materials are discussed. In addition, the design strategy of the additive species for the halide perovskite materials via the machine learning technique is provided. Suggestions to further develop the halide perovskite-based systems via the machine learning methods in the future are provided.

卤化钙钛矿材料是太阳能电池和光电器件的极佳候选材料。近年来，机器学习技术极大地促进了钙钛矿卤化物材料的设计，该技术有效地识别出合适的钙钛矿卤化物候选物，并通过模仿人类认知功能的算法揭示了隐藏的关系。在本文中，我们回顾了卤化钙钛矿材料的机器学习研究的最新进展，包括对无铅且稳定的卤化钙钛矿材料的预测和理解。讨论了描述卤化钙钛矿材料的性能和性能的结构描述符。此外，通过机器学习技术，提供了钙钛矿卤化物材料添加剂种类的设计策略。