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

{"title":"Machine learning-assisted design of high-performance perovskite photodetectors: a review","authors":"Xiaohui Li, Yongxiang Mai, Chunfeng Lan, Fu Yang, Putao Zhang, Shengjun Li","doi":"10.1007/s42114-024-01113-z","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":7220,"journal":{"name":"Advanced Composites and Hybrid Materials","volume":"8 1","pages":""},"PeriodicalIF":23.2000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Composites and Hybrid Materials","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s42114-024-01113-z","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}

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.

查看原文本刊更多论文

高性能钙钛矿光电探测器的机器学习辅助设计综述

基于钙钛矿材料的光电探测器（pd）已成为下一代光学传感领域的有力竞争者。由于它具有光电转换效率高、光谱响应宽、成本低、制备方便等优点，在光电子学领域具有广阔的应用前景。机器学习（ML）是人工智能的一个分支，它使计算机系统能够通过算法和统计模型自动从数据中提高性能。近年来，它已被用于光电器件的性能预测和材料筛选。因此，将ML和钙钛矿相结合以优化制造工艺和降低加工成本受到了广泛关注。本文综述了近年来在钙钛矿器件中应用ML的研究进展，分析了不同类型的钙钛矿材料在pd中的应用，并讨论了ML在钙钛矿pd中应用的可行性和挑战。这篇综述概述了钙钛矿pd向无与伦比的性能基准发展的前瞻性观点和路线图，为这一有前途的技术的未来发展轨迹提供了见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

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.