Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices  

作　　者：Bonghyun Jo Wenning Chen Hyun Suk Jung 

机构地区：[1]School of Advanced Materials Science and Engineering,Sungkyunkwan University,Suwon 16419,Republic of Korea [2]SKKU Institute of Energy Science and Technology(SIEST),Sungkyunkwan University,Suwon 16419,Republic of Korea [3]Department of Future Energy Engineering,Sungkyunkwan University,Suwon 16419,Republic of Korea

出　　处：《Journal of Energy Chemistry》2025年第2期298-323,I0007,共27页能源化学(英文版)

基　　金：supported by the Ministry of Science and ICT(MSIT)of the Republic of Korea(00302646);supported by the National Research Foundation of Korea grant funded by the Korean Government(MSIT)(NRF-2022R1A4A1019296,1345374646,2022M3J1A1064315).

摘　　要：Perovskite solar cells(PSCs)have developed rapidly,positioning them as potential candidates for nextgeneration 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 machinelearning(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.

关 键 词：Perovskite solar cell Data-driven machine learning CHARACTERIZATION Perovskite materials 

分 类 号：TB34[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程] TM914.4[自动化与计算机技术—控制科学与工程]