Machine learning-assisted design of high-performance perovskite photodetectors: a review

IF 23.2 2区材料科学 Q1 MATERIALS SCIENCE, COMPOSITES

Advanced Composites and Hybrid Materials Pub Date : 2024-12-06 DOI:10.1007/s42114-024-01113-z

Xiaohui Li, Yongxiang Mai, Chunfeng Lan, Fu Yang, Putao Zhang, Shengjun Li

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引用次数: 0

Abstract

Photodetectors (PDs) based on perovskite materials have become a strong contender for next-generation optical sensing. Because it has the advantages of high photoelectric conversion efficiency, broad spectral response, low cost, and easy preparation, it has a promising application in the field of optoelectronics. Machine learning (ML) is a branch of artificial intelligence that enables computer systems to improve performance from data through algorithms and statistical models automatically. Recently, it has been used in performance prediction and material screening of optoelectronic devices. As a result, combining ML and perovskite PDs has received much attention to optimize manufacturing processes and reduce processing costs. In this review, we provide a comprehensive review of recent research advances in the use of ML for perovskite devices, analyze the application of different types of perovskite materials in PDs, and discuss the feasibility and challenges of applying ML in perovskite PDs. This review outlines a visionary perspective and a roadmap for the progression of perovskite PDs towards unparalleled performance benchmarks, offering insights into the future trajectory of this promising technology.

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来源期刊

Advanced Composites and Hybrid Materials Multiple-

CiteScore

26.00

自引率

21.40%

发文量

185

期刊介绍： Advanced Composites and Hybrid Materials is a leading international journal that promotes interdisciplinary collaboration among materials scientists, engineers, chemists, biologists, and physicists working on composites, including nanocomposites. Our aim is to facilitate rapid scientific communication in this field. The journal publishes high-quality research on various aspects of composite materials, including materials design, surface and interface science/engineering, manufacturing, structure control, property design, device fabrication, and other applications. We also welcome simulation and modeling studies that are relevant to composites. Additionally, papers focusing on the relationship between fillers and the matrix are of particular interest. Our scope includes polymer, metal, and ceramic matrices, with a special emphasis on reviews and meta-analyses related to materials selection. We cover a wide range of topics, including transport properties, strategies for controlling interfaces and composition distribution, bottom-up assembly of nanocomposites, highly porous and high-density composites, electronic structure design, materials synergisms, and thermoelectric materials. Advanced Composites and Hybrid Materials follows a rigorous single-blind peer-review process to ensure the quality and integrity of the published work.