{"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 , Wenning Chen , 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 , Wenning Chen , 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}
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.
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