应用化学2025，Vol.42Issue(6)：757-775,19.DOI:10.19894/j.issn.1000-0518.250064
机器学习驱动的钙钛矿发光材料研究进展:智能设计、性能优化与产业化应用
Research Progress of Machine Learning-Driven Perovskite Luminescent Materials:Intelligent Design,Performance Optimization and Industrial Application
摘要
Abstract
Machine learning techniques provide a breakthrough solution for the efficient development of perovskite light-emitting materials.This paper systematically reviews the innovative applications of this technology in material design,performance optimization,and experimental guidance,focusing on solving the bottlenecks of the inefficiency of the traditional trial-and-error method(320 experiments are required for the optimization of a single component of CsPbBr3,which takes 14 months)and the insufficient accuracy of theoretical calculations(the error in the prediction of the luminous efficiency of the Eu3+-doped system by DFT is as high as 40%).In terms of material structure and property prediction,cross-scale modeling based on neural networks has significantly improved the prediction accuracy of key parameters.The developed deep convolutional neural network(CNN)model analyzes crystal distortion through a 128×128×128 electron density grid,achieving a mean absolute error(MAE)of 0.08 eV for band gap prediction,with a prediction error of only 1.3%for CsPbBr3.The graph neural network(GNN)further quantifies the correlation between the stacking angle of layered perovskites and the band gap,with a prediction error of＜0.05 eV.In material screening and optimization,the multi-objective algorithm achieves a synergistic improvement in performance indicators.The NSGA-II algorithm was used to screen Cs2 SnGeI6,which achieved an external quantum efficiency(EQE)of 24%and extended the device lifetime(T₅₀)to 1200 h.The Bayesian optimization framework combined with a robotic platform increased the photoluminescence quantum yield(PLQY)of CsPbBr3-x Ix quantum dots from 45%to 89%,and the number of experimental iterations was reduced by 85%.In terms of experimental design,the microfluidic robotic platform screened the optimal ratio of CsPbBr1.5 I1.5 within 24 h by dynamically adjusting the parameters(flow rate 10～100 μL/min,mixing time 110 s),with an emission wavelength error of±3 nm and a PLQY of 92%.In the process optimization of flexible devices,the Bayesian algorithm increased the photoelectric conversion efficiency(PCE)from 18.2%to 21.5%,and the process time was shortened by 62.5%.关键词
机器学习/钙钛矿材料/智能材料筛选/应用研究Key words
Machine learning/Perovskite materials/Intelligent material screening/Application research分类
化学引用本文复制引用
王一铭,杨博翔,张华久,孙立恒,董彪..机器学习驱动的钙钛矿发光材料研究进展:智能设计、性能优化与产业化应用[J].应用化学,2025,42(6):757-775,19.基金项目
国家自然科学基金(Nos.52250077,52272080)、吉林省自然科学基金(No.20220402005GH)和吉林省科技厅项目(No.20210204095YY)资助 Supported by the National Natural Science Foundation of China(Nos.52250077,52272080),the Natural Science Foundation of Jilin Province(No.20220402005GH)and Jilin Provincial Department of Science and Technology(No.20210204095YY) （Nos.52250077,52272080）
