Research Progress of Machine Learning-Driven Perovskite Luminescent Materials： Intelligent Design， Performance Optimization and Industrial Application

doi:10.19894/j.issn.1000-0518.250064

Chinese Journal of Applied Chemistry ›› 2025, Vol. 42 ›› Issue (6): 757-775.DOI: 10.19894/j.issn.1000-0518.250064

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Research Progress of Machine Learning-Driven Perovskite Luminescent Materials： Intelligent Design， Performance Optimization and Industrial Application

Yi-Ming WANG¹, Bo-Xiang YANG², Hua-Jiu ZHANG³, Li-Heng SUN¹(), Biao DONG¹()

^1.College of Electronic Science and Engineering，Jilin University，Changchun 130012，China
^2.College of Chemistry，Jilin University，Changchun 130015，China
^3.Affiliated Middle School to Jilin University，Changchun 130021，China

Received:2025-02-21 Accepted:2025-05-11 Published:2025-06-01 Online:2025-07-01
Contact: Li-Heng SUN,Biao DONG
About author:dongb@jlu.edu.cn
sunlh24@jlu.edu.cn
Supported by:
the National Natural Science Foundation of China(52250077);the Natural Science Foundation of Jilin Province(20220402005GH);Jilin Provincial Department of Science and Technology(20210204095YY)

Abstract

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 Cs₂SnGeI₆， which achieved an external quantum efficiency （EQE） of 24% and extended the device lifetime （T??） to 1200 h. The Bayesian optimization framework combined with a robotic platform increased the photoluminescence quantum yield （PLQY） of CsPbBr_3-xI_x 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 CsPbBr_1.5I_1.5 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%.

Key words: Machine learning, Perovskite materials, Intelligent material screening, Application research

CLC Number:

O611.6

Yi-Ming WANG, Bo-Xiang YANG, Hua-Jiu ZHANG, Li-Heng SUN, Biao DONG. Research Progress of Machine Learning-Driven Perovskite Luminescent Materials： Intelligent Design， Performance Optimization and Industrial Application[J]. Chinese Journal of Applied Chemistry, 2025, 42(6): 757-775.

Figures/Tables 8

Fig.1 Machine learning for perovskite luminescent materials with multidimensional assisted orientation

Fig.2 Flowchart of machine learning for 2D OIHPs （2D organic-inorganic halide perovskites）［29］

Fig.3 Experimental flowchart for the preparation of perovskite thin-film devices using machine learning methods［32］

Fig.4 （A） Flowchart of the ML（Machine learning） and BO （Bayesian optimization） machine learning processes used to optimize PLQY；（B） Model accuracy of the Gaussian process regressor after the first iteration， taking 24 data points and reporting the MSE and R2 scores；（C） Evolution of PLQY as a cluster plot over 2 iterations of BO， with optimal values for each batch shown above；（D） 3D scatterplot of the Yb/Pb， temperature， and Cs/Pb variables， with the PLQY values coloring. The highest PLQY values reported in the upper left corner correspond to high Yb and high temperatures；（E） Power-dependent PLQY measurements by BO on optimized Yb-doped CsPbCl3 nanocrystalline samples［37］

Fig.5 Convolutional neural network （CNN） model construction process［38］

Fig.6 Experimental flowchart for accelerating the discovery and sustainable synthesis of perovskite solid solutions using machine learning［46］

Fig.7 A machine learning-based solution for the experimental optimization process［47］

Fig.8 Training mechanisms for traditional machine learning and graph neural network based models and a comparison of the two［55］

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Research Progress of Machine Learning-Driven Perovskite Luminescent Materials： Intelligent Design， Performance Optimization and Industrial Application

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