Machine learning for halide perovskite materials. (December 2020)

• Record Type: Journal Article
• Authors: Zhang, Lei; He, Mu; Shao, Shaofeng
• Is Part Of: Nano energy. Volume 78(2020)
• Publication Date: 2020-12
• Subjects: Halide perovskites -- Machine learning -- Solar cells; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2020.105380
• Languages: English
• ISSNs: 2211-2855

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: Machine learning halide perovskite materials is reviewed. Machine learning accelerates the design of lead-free and stable halide perovskites. Suitable descriptors are critical to find candidates and unveil hidden relationships. The design of compatible additives is facilitated by machine learning.