机器学习驱动的光伏设备用包晶石材料优化和表征进展综述

IF 13.1 1区 化学 Q1 Energy Journal of Energy Chemistry Pub Date : 2024-10-02 DOI:10.1016/j.jechem.2024.09.043
Bonghyun Jo , Wenning Chen , Hyun Suk Jung
{"title":"机器学习驱动的光伏设备用包晶石材料优化和表征进展综述","authors":"Bonghyun Jo ,&nbsp;Wenning Chen ,&nbsp;Hyun Suk Jung","doi":"10.1016/j.jechem.2024.09.043","DOIUrl":null,"url":null,"abstract":"<div><div>Perovskite solar cells (PSCs) have developed rapidly, positioning them as potential candidates for next-generation renewable energy sources. However, conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based optoelectronics. The increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machine-learning (ML) approaches to swiftly advance in the materials field. This review succinctly outlines the fundamental ML procedures, techniques, and recent breakthroughs, particularly in predicting the physical characteristics of perovskite materials. Moreover, it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of PSCs. Additionally, this review highlights recent efforts in using characterization data for ML, exploring their correlations with material properties and device performance, which are actively being researched, but they have yet to receive significant attention. Lastly, we provide future perspectives, such as leveraging Large Language Models (LLMs) and text-mining, to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.</div></div>","PeriodicalId":15728,"journal":{"name":"Journal of Energy Chemistry","volume":"101 ","pages":"Pages 298-323"},"PeriodicalIF":13.1000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices\",\"authors\":\"Bonghyun Jo ,&nbsp;Wenning Chen ,&nbsp;Hyun Suk Jung\",\"doi\":\"10.1016/j.jechem.2024.09.043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Perovskite solar cells (PSCs) have developed rapidly, positioning them as potential candidates for next-generation renewable energy sources. However, conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based optoelectronics. The increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machine-learning (ML) approaches to swiftly advance in the materials field. This review succinctly outlines the fundamental ML procedures, techniques, and recent breakthroughs, particularly in predicting the physical characteristics of perovskite materials. Moreover, it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of PSCs. Additionally, this review highlights recent efforts in using characterization data for ML, exploring their correlations with material properties and device performance, which are actively being researched, but they have yet to receive significant attention. Lastly, we provide future perspectives, such as leveraging Large Language Models (LLMs) and text-mining, to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.</div></div>\",\"PeriodicalId\":15728,\"journal\":{\"name\":\"Journal of Energy Chemistry\",\"volume\":\"101 \",\"pages\":\"Pages 298-323\"},\"PeriodicalIF\":13.1000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Energy Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2095495624006673\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Energy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Energy Chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095495624006673","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0

摘要

包光体太阳能电池(PSCs)发展迅速,已成为下一代可再生能源的潜在候选材料。然而,传统的试错方法和庞大的组成参数空间仍然是追求基于包晶石的光电器件的卓越性能和高稳定性的挑战。光电器件对新型材料的需求日益增长,大量数据库的建立使得数据驱动的机器学习(ML)方法在材料领域迅速发展。本综述简明扼要地概述了机器学习的基本程序、技术和最新突破,尤其是在预测包晶材料的物理特性方面。此外,它还重点介绍了旨在优化和筛选材料以提高 PSC 效率和稳定性的研究工作。此外,本综述还重点介绍了最近在使用表征数据进行 ML、探索其与材料特性和器件性能的相关性方面所做的努力。最后,我们提出了未来的展望,例如利用大型语言模型(LLM)和文本挖掘,加快新型光致发光材料的发现,并扩大其在各个光电领域的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices
Perovskite solar cells (PSCs) have developed rapidly, positioning them as potential candidates for next-generation renewable energy sources. However, conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based optoelectronics. The increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machine-learning (ML) approaches to swiftly advance in the materials field. This review succinctly outlines the fundamental ML procedures, techniques, and recent breakthroughs, particularly in predicting the physical characteristics of perovskite materials. Moreover, it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of PSCs. Additionally, this review highlights recent efforts in using characterization data for ML, exploring their correlations with material properties and device performance, which are actively being researched, but they have yet to receive significant attention. Lastly, we provide future perspectives, such as leveraging Large Language Models (LLMs) and text-mining, to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Energy Chemistry
Journal of Energy Chemistry CHEMISTRY, APPLIED-CHEMISTRY, PHYSICAL
CiteScore
19.10
自引率
8.40%
发文量
3631
审稿时长
15 days
期刊介绍: The Journal of Energy Chemistry, the official publication of Science Press and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, serves as a platform for reporting creative research and innovative applications in energy chemistry. It mainly reports on creative researches and innovative applications of chemical conversions of fossil energy, carbon dioxide, electrochemical energy and hydrogen energy, as well as the conversions of biomass and solar energy related with chemical issues to promote academic exchanges in the field of energy chemistry and to accelerate the exploration, research and development of energy science and technologies. This journal focuses on original research papers covering various topics within energy chemistry worldwide, including: Optimized utilization of fossil energy Hydrogen energy Conversion and storage of electrochemical energy Capture, storage, and chemical conversion of carbon dioxide Materials and nanotechnologies for energy conversion and storage Chemistry in biomass conversion Chemistry in the utilization of solar energy
期刊最新文献
Catalytic production of high-energy-density spiro polycyclic jet fuel with biomass derivatives Metallized polymer current collector as “stress acceptor” for stable micron-sized silicon anodes Microdynamic modulation through Pt–O–Ni proton and electron “superhighway” for pH-universal hydrogen evolution High-areal-capacity and long-life sulfide-based all-solid-state lithium battery achieved by regulating surface-to-bulk oxygen activity Introducing strong metal–oxygen bonds to suppress the Jahn-Teller effect and enhance the structural stability of Ni/Co-free Mn-based layered oxide cathodes for potassium-ion batteries
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1